Evaluating public records of supply transactions

ABSTRACT

A platform facilitates buyers, sellers, and third parties in obtaining information related to each other&#39;s transaction histories, such as a supplier&#39;s shipment history, the types of materials typically shipped, a supplier&#39;s customers, a supplier&#39;s expertise, what materials and how much a buyer purchases, buyer and shipper reliability, similarity between buyers, similarity between suppliers, and the like. The platform aggregates data from a variety of sources, including, without limitation, customs data associated with actual import/export transactions and facilitates the generation of reports as to the quality of buyers and suppliers, the reports relating to a variety of parameters that are associated with buyer and supplier quality.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.12/271,593 filed Nov. 14, 2008, which claims the benefit of U.S.Provisional App. No. 60/987,989 filed Nov. 14, 2007, each of which ishereby incorporated by reference in its entirety.

BACKGROUND Field

The present invention is related to electronic commerce, and moreparticularly to rating systems.

Description of the Related Art:

Buyers who are interested in working with suppliers, particularlyoverseas suppliers, may have many suppliers from which they can choose.For instance, in the apparel industry there are an estimated 40,000apparel factories in China alone, with some 80,000 worldwide. In orderto select a supplier, a buyer traditionally has had to rely on directexperience with the supplier or work through a middleman thatfacilitates contracting with suppliers. However, working with amiddle-man may incur commissions for their services, and workingdirectly with the supplier may present the buyer with a large degree ofuncertainty, such as relating to the quality and reliability of thesupplier, who the supplier typically works with, what type of productsthe supplier typically supplies, materials used, customers served, andthe like. Some information about suppliers can be obtained from othersources, such as trade fairs, online directories, referrals, and thelike. These disparate sources of information are, however, difficult tosort through, and at present there is a distinct lack of reliable andobjective information that buyers can use to assess suppliers around theworld. As a result, buyers must proceed largely on their own, and atconsiderable risk and expense.

A need exists for ways for buyers to more easily select suppliers.

SUMMARY

Methods and systems are disclosed herein for a platform by which buyers,sellers, and third parties can obtain information related to eachother's transaction histories, such as a supplier's shipment history,the types of materials typically shipped, a supplier's customers, asupplier's expertise, what materials and how much a buyer purchases,buyer and shipper reliability, similarity between buyers, similaritybetween suppliers, and the like. The platform may aggregate data from avariety of sources, including, without limitation, customs dataassociated with actual import/export transactions, and facilitates thegeneration of reports as to the quality of buyers and suppliers, thereports relating to a variety of parameters that are associated withquality buyers and suppliers, and the like.

In an aspect of the invention, methods and systems may include: using acomputer implemented facility to collect and store a plurality ofrecords of transactions among a plurality of buyers and a plurality ofsuppliers; aggregating the transactions; associating the transactionswith entities; and rating an entity based on analysis of the aggregatedtransactions. In the aspect a rating is tailored based on criteriadefined by an end user.

In the aspect a rating is for one or more of: suppliers using aggregatedtransactional customs data, a supplier based on customs data related totransactions by the supplier with a third party, a buyer usingaggregated transactional customs data, a buyer based on customs datarelated to transactions of the buyer with a third party, a supplierbased on loyalty as indicated by analysis of customs transactions, asupplier based on amount of experience as indicated by customstransactions, a supplier based on evaluating the number of shipments, asupplier based on duration of experience as indicated by shipments, asupplier based on size of transactions as indicated by past shipments, asupplier based on extent of international experience as indicated bypast shipments, a supplier based on extent of country-relevantexperience as indicated by past shipments, a buyer based on loyalty asindicated by analysis of customs transactions, a buyer based on amountof experience as indicated by customs transactions, a buyer based onevaluating the number of shipments, a buyer based on duration ofexperience as indicated by shipments, a buyer based on size oftransactions as indicated by past shipments, a buyer based on extent ofinternational experience as indicated by past shipments, a buyer basedon extent of country-relevant experience as indicated by past shipments,a supplier based on customer loyalty and supplier experience asindicated by past shipments reflected in customs records.

In the aspect, the rating is further based on at least two factorsselected from the group consisting of: a country context of a party, abusiness legitimacy of a party, whether a party is registered withgovernment authorities, an assessment of a trading environment in acountry, macroeconomic information, public recognition of a party,industry awards, industry certifications, amount of experience, numberof shipments, duration of experience, size of transactions, extent ofdomestic experience, extent of international experience, caliber ofcustomers, customer loyalty, degree of specialization, specialization inproduct categories, specialization in manufacturing techniques,specialization in materials, specialization in gender, feedback fromcustomers, feedback from buyers, feedback on product quality, feedbackon customer service, feedback on timeliness of delivery, feedback onlanguage skills, feedback on sample making ability, respect forintellectual property, quality management, social responsibility,environmental responsibility, standards of compliance, certifications,and certifications with respect to specific vendor standards.

In the aspect, the rating is based on one of: a country context of aparty, a business legitimacy of a party, whether a party is registeredwith government authorities, an assessment of a trading environment in acountry, macroeconomic information, public recognition of a party,industry awards, industry certifications, amount of experience, numberof shipments, duration of experience, size of transactions, extent ofdomestic experience, extent of international experience, caliber ofcustomers, customer loyalty, degree of specialization, specialization inproduct categories, specialization in manufacturing techniques,specialization in materials, specialization in gender, feedback fromcustomers, feedback from buyers, feedback on product quality, feedbackon customer service, feedback on timeliness of delivery, feedback onlanguage skills, feedback on sample making ability, respect forintellectual property, quality management, social responsibility,environmental responsibility, standards of compliance, certifications,and certifications with respect to specific vendor standards

In the aspect, weights are given in the rating process. The weights arebased on timeliness of data. The weights are given based on size oftransaction. The weights for transactions are given based on the qualityof the transacting parties; the quality of a transacting party is basedon a prior rating for that party. The weights are based on relevance ofdata.

In the aspect the rating is for a plurality of factories of an entity.

In the aspect the rating includes providing a human-aided assessment ofsupplier skills as a factor in a rating. Alternatively the ratingincludes using an indicator of an entity's financial health as a factorin a rating.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of records ofcustoms transactions among a plurality of buyers and a plurality ofsuppliers; aggregating the transactions; associating the transactionswith entities; and providing an entity score for an entity based onanalysis of the aggregated transactions. In the aspect the entity scoreis based at least in part on transactional data about shipments by theentity. In the aspect the entity score includes factors selected fromthe group consisting of country context, business legitimacyinformation, public recognition, amount of experience, caliber ofcustomers of the supplier, customer loyalty for the supplier, degree ofspecialization of the supplier, and feedback from previous customers. Inthe aspect the entity score for the suppliers is based on aggregatedtransactional customs data. In the aspect the entity score is based on acriteria defined by an end user. In the aspect the entity score for asupplier is based on customs data related to transactions by thesupplier with a third party. In the aspect the entity score is based onat least two factors selected from the group consisting of: a countrycontext of a party, a business legitimacy of a party, whether a party isregistered with government authorities, an assessment of a tradingenvironment in a country, macroeconomic information, public recognitionof a party, industry awards, industry certifications, amount ofexperience, number of shipments, duration of experience, size oftransactions, extent of domestic experience, extent of internationalexperience, caliber of customers, customer loyalty, degree ofspecialization, specialization in product categories, specialization inmanufacturing techniques, specialization in materials, specialization ingender, feedback from customers, feedback from buyers, feedback onproduct quality, feedback on customer service, feedback on timeliness ofdelivery, feedback on language skills, feedback on sample makingability, respect for intellectual property, quality management, socialresponsibility, environmental responsibility, standards of compliance,certifications, and certifications with respect to specific vendorstandards. In the aspect the entity score is based upon one of: acountry context of a party, a business legitimacy of a party, whether aparty is registered with government authorities, an assessment of atrading environment in a country, macroeconomic information, publicrecognition of a party, industry awards, industry certifications, amountof experience, number of shipments, duration of experience, size oftransactions, extent of domestic experience, extent of internationalexperience, caliber of customers, customer loyalty, degree ofspecialization, specialization in product categories, specialization inmanufacturing techniques, specialization in materials, specialization ingender, feedback from customers, feedback from buyers, feedback onproduct quality, feedback on customer service, feedback on timeliness ofdelivery, feedback on language skills, feedback on sample makingability, respect for intellectual property, quality management, socialresponsibility, environmental responsibility, standards of compliance,certifications, and certifications with respect to specific vendorstandards.

In the aspect, the entity score for a supplier is based on one or moreof: loyalty as indicated by analysis of customs transactions, an amountof experience as indicated by customs transactions, evaluating thenumber of shipments, a duration of experience as indicated by shipments,size of transactions as indicated by past shipments, extent ofinternational experience as indicated by past shipments, on extent ofcountry-relevant experience as indicated by past shipments, and customerloyalty and supplier experience as indicated by past shipments reflectedin customs records. In the aspect the entity score for a buyer is basedon one or more of: loyalty as indicated by analysis of customstransactions, an amount of experience as indicated by customstransactions, evaluating the number of shipments, a duration ofexperience as indicated by shipments, a size of transactions asindicated by past shipments, an extent of international experience asindicated by past shipments, aggregated transactional customs data,customs data related to transactions of the buyer with a third party,and an extent of country-relevant experience as indicated by pastshipments. In the aspect the entity score is for a plurality offactories of an entity. In the aspect, methods and systems furtherinclude providing a human-aided assessment of supplier skills as afactor in a entity score or include using an indicator of an entity'sfinancial health as a factor in a entity score.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of records ofcustoms transactions among a plurality of buyers and a plurality ofsuppliers; aggregating the transactions; associating the transactionswith entities; and determining a risk profile based on analysis of theaggregated transactions. In the aspect the risk profile is provided withrespect to a supplier based on transactional customs data for thesupplier. In the aspect, the risk is related to at least one of:counterfeiting, capacity, subcontracting, a political factor, ageographic factor, a weather factor, a geology factor, a financial risk,a probability of non-performance of a contract, a probability oftermination of a contract, intellectual property, achieving a targeteddelivery date. In the aspect the risk profile is provided with respectto a supplier based on transactional customs data for a party other thanthe supplier. In this aspect, the risk is related to at least one of:counterfeiting, capacity, subcontracting, a political factor, ageographic factor, a weather factor, a geology factor, a financial risk,a probability of non-performance of a contract, a probability oftermination of a contract, intellectual property, achieving a targeteddelivery date.

In the aspect, the risk profile is provided with respect to a buyerbased on transactional customs data for the buyer. The risk is relatedto non-payment or the likelihood that a buyer will move to analternative supplier.

In the aspect, the risk profile is provided with respect to a buyerbased on transactional customs data for a party other than the buyer.The risk is related to non-payment or the likelihood that a buyer willmove to an alternative supplier.

In the aspect, the risk profile is provided for a party using customsdata and using the risk profile as a basis for determining terms andconditions of insurance.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of records ofcustoms transactions among a plurality of buyers and a plurality ofsuppliers; aggregating the transactions; associating the transactionswith entities; and providing an indicator of economic leverage withrespect to an entity based on analysis of the aggregated transactions.In the aspect, the indicator of economic leverage is with respect to atleast one of: a supplier based on transactional customs data for thesupplier, a supplier based on transactional customs data for a partyother than the supplier, a buyer based on transactional customs data forthe buyer, a buyer based on transactional customs data for a party otherthan the buyer. In the aspect, the transactional customs datacorresponds to a price. Alternatively in the aspect, the transactionalcustoms data corresponds to a delivery date or an order quantity.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include using a computerimplemented facility to collect and store a plurality of records ofcustoms transactions among a plurality of buyers and a plurality ofsuppliers; aggregating the transactions; associating the transactionswith entities; and predicting an action of an entity based on analysisof the aggregated transactions. In the aspect the prediction is of anaction of a buyer based on analysis of customs data for transactions bythe buyer. In the aspect the prediction is related to at least one of:price, a change in price, a change in supplier, and a quantity orderedby the buyer. In the aspect the prediction is of an action of a buyerbased on analysis of customs data for transactions by a party other thanthe buyer. The prediction is related to a price, a change in price, achange in supplier, or a quantity ordered by a buyer. In the aspect, theprediction is of an action of a supplier based on analysis of customsdata for transactions by the buyer. The prediction is related to aprice, a change in price, a change in availability of an item, whether asupplier will work with a buyer of a given size, or whether a supplierwill work with orders of a given size. In the aspect the prediction isof an action of a supplier based on analysis of customs data fortransactions by a party other than the buyer. The prediction is relatedto a price, a change in price, a change in availability of an item, apotential closure of a subsidiary, a potential closure of a factory, ora potential closure of a company.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of records ofcustoms transactions among a plurality of buyers and a plurality ofsuppliers; aggregating the transactions; associating the transactionswith entities; and making a recommendation based on analysis of theaggregated transactions. In the aspect the recommendation is based onanalysis of customs data for transactions by the buyer, analysis ofcustoms data for transactions by a party other than the buyer, analysisof customs data for transactions by the buyer, analysis of customs datafor transactions by a party other than the buyer, prioritization offactors by a user, or a user-specified rating factor.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of records oftransactions among a plurality of buyers and a plurality of suppliers;aggregating the transactions; associating the transactions withentities; and associating an entity type with at least one of theentities. In the aspect, a data merging facility automatically mergesrecords based on similarity of data elements with a customs record. Thedata elements correspond to a name of the entity or an address of theentity. Alternatively in the aspect, a data merging facility suggests anassociation between records and a single entity. The entity type isderived from one or more commodity fields in the transactions, and atleast one of the commodity fields includes a harmonic tariff systemcode, a commodity type, or both. In the aspect, associating an entitytype is based on an analysis of free text data in a plurality of datafields of the transactions. Associating an entity may alternatively bebased on machine learning of entity types from customs transactionaldata records. The transactions may be customs transactions.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of records ofcustoms transactions among a plurality of buyers and a plurality ofsuppliers; aggregating the transactions; and processing the data toassociate a plurality of transactions associated with a plurality ofdifferent entity names to a single entity based on analysis of customsrecord data for the transactions associated with the plurality ofdifferent entity names. In the aspect, the processing is based on, aname of the supplier, a name of the buyer, an order quantity, a billingamount, a location of the buyer, a location of the supplier, a deliverydate, order data, at least one string associated with a supplier name,or at least one string associated with a buyer name. In the aspect theprocessing involves removing blank spaces from a supplier name field orremoving blank spaces from a buyer name field. In the aspect, thetransaction is associated with a region of interest, an industry, pastshipment data, a country-relevant experience, a number of shipments, amaterial, a product category, a technique, a name of the entity, anorder quantity, a billing address, a targeted delivery date, or acapacity of the supplier.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of public recordsof transactions among a plurality of buyers and a plurality ofsuppliers; aggregating the transactions; associating the transactionswith entities; and evaluating legitimacy of feedback about an entitybased on analysis of whether the feedback is associated with atransaction reflected in public records. The evaluation of legitimacy offeedback associated with the supplier is based on validation by a thirdparty. The evaluation of legitimacy of feedback associated with thebuyer is based on validation by a third party.

In the aspect the transaction is associated with a name of the entity,an order quantity, a billing address, a targeted delivery date, acapacity of the supplier, transaction customs data, a region ofinterest, an industry, a past shipment, a country-relevant experience, anumber of shipments, a material, a product category, or a technique.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of public recordsof transactions among a plurality of buyers and a plurality ofsuppliers; aggregating the transactions; associating the transactionswith entities; and providing a computer-implemented tool for suggestinga marketing strategy for a supplier based on analysis of transactionaldata from the public records. In the aspect the transactional data isassociated with a supplier, a buyer, region of interest, customs data,past shipment, country relevant experience, a number of shipments, aproduct category, a material, or a technique. The analysis oftransactional data includes analysis of pricing, buyer behavior, ortransactional data associated with a competitor of the supplier.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of records oftransactions among a plurality of buyers and a plurality of suppliers;aggregating the transactions;

associating the transactions with entities; and providing acomputer-implemented tool for suggesting a marketing strategy for abuyer based on analysis of the transactional data from the records. Inthe aspect, the transactional data is associated with a supplier, abuyer, a region of interest, customs data, a past shipment, a countryrelevant experience, a number of shipments, a product category, amaterial, or a technique. The analysis of transactional data includesanalysis of pricing, buyer behavior, or analysis of transactional dataassociated with a competitor of the buyer.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of public recordsof transactions among a plurality of buyers and a plurality ofsuppliers; aggregating the transactions; associating the transactionswith entities; and providing a user interface whereby a user may searchfor at least one of a supplier and a buyer and retrieve relevantinformation based on the aggregated transactions data. In the aspect,the interface allows a tuple-based search. The tuple-based searchrelates to a capability with respect to at least one of a product, amaterial and a technique. In the aspect, search results are ranked basedon a supplier rating.

In the aspect, the rating is based upon a country context of a party,business legitimacy of a party, whether a party is registered withgovernment authorities, an assessment of a trading environment in acountry, macroeconomic information, public recognition of a party,industry awards, industry certifications, amount of experience, numberof shipments, duration of experience, size of transactions, extent ofdomestic experience, extent of international experience, caliber ofcustomers, customer loyalty, degree of specialization, specialization inproduct categories, specialization in manufacturing techniques,specialization in materials, specialization in gender, feedback fromcustomers, feedback from buyers, feedback on product quality, feedbackon customer service, feedback on timeliness of delivery, feedback onlanguage skills, feedback on sample making ability, respect forintellectual property, quality management, social responsibility,environmental responsibility, standards of compliance, certifications,or certifications with respect to specific vendor standards.

In the aspect, the search results are based on a risk profile.

In the aspect, the risk is related to counterfeiting, capacity,subcontracting, a political factor, a geographic factor, a weatherfactor, a geology factor, a financial risk, a probability ofnon-performance of a contract, a probability of termination of acontract, intellectual property, achieving a targeted delivery date.

In the aspect, the risk profile is provided with respect to a supplierbased on transactional customs data for a party other than the supplier.The risk is related to counterfeiting, capacity, subcontracting, apolitical factor, a geographic factor, a weather factor, a geologyfactor, a financial risk, a probability of non-performance of acontract, a probability of termination of a contract, intellectualproperty, achieving a targeted delivery date, a buyer based ontransactional customs data for the buyer, non-payment, or the likelihoodthat a buyer will move to an alternative supplier. In the aspect, therisk profile is provided with respect to a buyer based on transactionalcustoms data for a party other than the buyer. The risk is related tonon-payment or the likelihood that a buyer will move to an alternativesupplier.

In the aspect, the risk profile is provided for a party using customsdata and using the risk profile as a basis for determining terms andconditions of insurance. The results are based on an opportunityprofile. The opportunity relates to the availability of pricing leveragefor a buyer with respect to a supplier, consolidation of orders with asupplier. The opportunity relates to the availability of pricingleverage for a supplier with respect to a buyer or to increasing a shareof a buyer's total spending for a supplier.

In another aspect of the invention, the methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of public recordsof transactions among a plurality of buyers and a plurality ofsuppliers; aggregating the transactions; associating the transactionswith entities; integrating the aggregated and associated transactionswith data from at least on3 other data source to provide an integrateddata facility; and adapting the integrated data facility for evaluatingat least one of a supplier and a buyer. In the aspect, the publicrecords include customs records. In the aspect, evaluations are rankedbased on a supplier rating. In the aspect, the evaluation is based upona country context of a party, business legitimacy of a party, whether aparty is registered with government authorities, an assessment of atrading environment in a country, macroeconomic information, publicrecognition of a party, industry awards, industry certifications, amountof experience, number of shipments, duration of experience, size oftransactions, extent of domestic experience, extent of internationalexperience, caliber of customers, customer loyalty, degree ofspecialization, specialization in product categories, specialization inmanufacturing techniques, specialization in materials, specialization ingender, feedback from customers, feedback from buyers, feedback onproduct quality, feedback on customer service, feedback on timeliness ofdelivery, feedback on language skills, feedback on sample makingability, respect for intellectual property, quality management, socialresponsibility, environmental responsibility, standards of compliance,certifications, or certifications with respect to specific vendorstandards.

In the aspect, evaluations are ranked based on a buyer rating, a countrycontext of a party, business legitimacy of a party, whether a party isregistered with government authorities, an assessment of a tradingenvironment in a country, macroeconomic information, public recognitionof a party, industry awards, industry certifications, amount ofexperience, number of shipments, duration of experience, size oftransactions, extent of domestic experience, extent of internationalexperience, caliber of customers, customer loyalty, degree ofspecialization, specialization in product categories, specialization inmanufacturing techniques, specialization in materials, specialization ingender, feedback from customers, feedback from buyers, feedback onproduct quality, feedback on customer service, feedback on timeliness ofdelivery, feedback on language skills, feedback on sample makingability, respect for intellectual property, quality management, socialresponsibility, environmental responsibility, standards of compliance,certifications, or certifications with respect to specific vendorstandards.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of records ofcustoms transactions among a plurality of buyers and a plurality ofsuppliers; aggregating the transactions; associating the transactionswith entities; and suggesting an opportunity based on analysis of thetransactions. In the aspect, the opportunity relates to the availabilityof pricing leverage for a buyer with respect to a supplier, anopportunity for consolidation of orders with a supplier, theavailability of pricing leverage for a supplier with respect to a buyer,the opportunity to increase a share of a buyer's total spending for asupplier, the availability of a discount for the buyer with respect tothe supplier for a specified period, the availability of a committedtime for delivery by the buyer to the supplier, the availability of bulkdiscount for the buyer with respect to the supplier, the availability ofcredit sales for the buyer with respect to the supplier, theavailability of free delivery for the buyer with respect to thesupplier, or the availability of liquidated damages for the buyer withrespect to the supplier.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of aggregatedcustoms transactions; associating the transactions with a supplier; andusing the aggregated transactions to inform a rating of the supplierbased at least in part on analysis of the aggregated transactions. Inthe aspect, the aggregated customs transactions include a summary oftransactions for a product type. The transactions are summarized over aperiod of time. The analysis of the aggregated transactions includescomparing the aggregated transactions for a supplier with a plurality ofrecords of transactions for a buyer. The aggregated customs transactionsinclude transactions for a plurality of suppliers. In the aspect,associating the transactions with a supplier includes predicting one ormore suppliers to which the transactions can be associated.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: taking a plurality ofinput data records from at least one data source of transactions;matching the data records to an entity that is a party to a plurality oftransactions; and automatically merging the data records associated withthe same entity to form a merged data store of transactions. In theaspect, matching includes filtering the data records. Filtering suggestsdata records for merging. Filtering is based on search enginetechniques, such as a lucene search engine technique. Filtering is basedon kgram filtering that may include a kgram filtering group thatconsists of four consecutive characters. In the aspect, a kgramfiltering threshold for suggesting data records to be merged is tenmatching kgram filter groups. In the aspect, a plurality of data fieldswithin a data record are combined for matching.

In the aspect, matching includes classification. Classification isperformed on data records suggested for merging and optionally,filtering is used to suggest data records for merging. Classificationincludes at least one of canonical adaptation, text cleanup, multi-fieldclassification, edit distance assessment, vector generation, machinelearning, and decision tree processing. Canonical adaptation includesnormalizing text strings among the plurality of transactions or changingequivalent text strings to a known text string. Text cleanup is based onat least one of geographic factors, regional factors, market verticals,industry norms, known variations, learned variations, and userpreferences.

In the aspect, the text cleanups are associated with at least one typeof data field in the data records. The type of data field includes atleast one of a shipper, a consignee, a notify party, an also notifyparty, a weight, a quantity, a country, a date, a commodity, and aharmonic tariff system code. Classification is applied to a plurality ofdata fields in the data records or to combined data fields in the datarecords.

In the aspect, classification provides a vector that representsdimensions of similarity. The vector includes dimensions of similarityfor at least two of canonical adaptation, text cleanup, multi-fieldclassification, edit distance assessment, vector generation, machinelearning, and decision tree processing.

In the aspect, matching includes clustering. Optionally, clusteringincludes p-percent clustering. In the aspect, a data record is mergedwhen a p-percent value associated with the data record exceeds ap-percent threshold associated with the entity. Optionally the p-percentthreshold is thirty percent. Alternatively p-percent clustering is basedon a dynamic p-percent threshold. The dynamic p-percent threshold isbased on a quantity of data records in a cluster associated with anentity.

In the aspect, the plurality of transactions contains party identifyingdata in a field of the data records. The party identifying data isstored in different fields of at least two of the plurality of datarecords. Optionally, party identifying data in a first record is aparent entity and a party identifying data in a second record is a childentity of the parent entity.

In the aspect, matching data records includes identifying data that is avariation of an entity name or an entity address. In the aspect, theparty is one of a supplier and a buyer. Alternatively, matching includestwo or more types of text association selected from a list consistingof: filtering, character group matching, thesaurus lookup, machinelearning, natural language processing, search-based comparison,classification, known entity matching, clustering, and human-identifiedentities.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: taking a plurality ofinput data records from at least one data source of transactions;filtering the input data records to identify a set of filtered datarecords that are favorable candidates for automatic merging; classifyingthe filtered data records to produce a set of classified data records,each classified data record associated with a likelihood that the datarecord should be associated with a particular entity; and automaticallymerging the data records associated with the same entity to form amerged data store of transactions. In the aspect, the filtering isperformed using a search engine, kgram filtering, or dynamicprogramming. I the aspect, classifying the data records is performedusing at least one of canonical adaptation, specific cleanups,multi-field comparison, an edit distance algorithm, vector generation,machine learning, and a decision tree. In the aspect, filtering suggestsdata records for merging. Alternatively filtering is based on searchengine techniques, that optionally include a lucene search enginetechnique. In the aspect filtering is based on kgram filtering.Optionally a kgram filtering group consists of four consecutivecharacters. Optionally a kgram filtering threshold for suggesting datarecords to be merged is ten matching kgram filter groups. In the aspect,a plurality of data fields within a data record is combined formatching.

In the aspect, classification is performed on data records suggested formerging. Optionally filtering is used to suggest data records formerging. In the aspect classification includes at least one of canonicaladaptation, text cleanup, multi-field classification, edit distanceassessment, vector generation, machine learning, and decision treeprocessing. Canonical adaptation includes normalizing text strings amongthe transactions or changing equivalent text strings to a known textstring.

In the aspect, text cleanup may be based on at least one of geographicfactors, regional factors, market verticals, industry norms, knownvariations, learned variations, and user preferences. Optionally, textcleanups are associated with at least one type of data field in the datarecords. The type of data field includes a shipper, a consignee, anotify party, an also notify party, a weight, a quantity, a country, adate, a commodity, and a harmonic tariff system code.

In the aspect, classification is applied to a plurality of data fieldsin the data records or to combined data fields in the data records.

In the aspect, classification provides a vector that representsdimensions of similarity. Optionally, the vector includes dimensions ofsimilarity for at least two of canonical adaptation, text cleanup,multi-field classification, edit distance assessment, vector generation,machine learning, and decision tree processing.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of records oftransactions among a plurality of buyers and a plurality of suppliers;aggregating the transactions; associating the transactions withentities; and classifying an entity as a buyer based on analysis of theaggregated transactions. In the aspect the aggregated transaction isassociated with an industry, customs data, a past shipment, a likelihoodof interest, or a number of shipments.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of records oftransactions among a plurality of buyers and a plurality of suppliers;aggregating the transactions; associating the transactions withentities; and using the transactions as a training set to predictassociation of a particular transaction with an attribute. In theaspect, the attribute is a type of industry, a type of supplier, a typeof product, a product attribute, or related to a type of material. Inthe aspect, the particular transaction represents a shipment from asupplier to a buyer. The transactions are customs transactions.Alternatively, the entities are one or more of a supplier and a buyer.Optionally, the particular transaction is a rolled-up transaction.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of records oftransactions among a plurality of buyers and a plurality of suppliers;aggregating the transactions; associating the transactions withentities; and using the transactions as a training set to predictassociation of a particular transaction with an entity. In the aspect,the particular transaction represents a shipment from a supplier to abuyer. Alternatively, the transactions are customs transactions. In theaspect, an entity is one of a supplier and a buyer. Optionally, theparticular transaction is a rolled-up transaction.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of records oftransactions among a plurality of buyers and a plurality of suppliers;aggregating the transactions; associating the transactions withentities; and predicting a minimum order requirement for an entity basedon analysis of the transactions. In the aspect the entity is a factory,supplier, or a subsidiary of a supplier.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of records oftransactions among a plurality of buyers and a plurality of suppliers;aggregating the transactions; associating the transactions withentities; and providing a search facility for enabling a search for anentity, wherein the search facility allows searching based on geographicregion, industry specialization, entities participating in thetransactions, and likelihood of interest in a transaction with thesearcher. In the aspect, the search facility is adapted to be used by abuyer searching for a supplier or adapted to be used by a suppliersearching for a buyer.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of records oftransactions among a plurality of buyers and a plurality of suppliers;aggregating the transactions; associating the transactions withentities; and rating a sub-entity of a supplier based on analysis of theaggregated transactions. In the aspect, the sub-entity is a factory, acollection of factories, or a subsidiary. In the aspect, determining thesub-entity is based on analysis of the public records. Optionally, thepublic records are records of customs transactions.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of aggregatedpublic records of shipment transactions; associating the transactionswith a supplier; and using the aggregated transactions to inform arating of the supplier based at least in part on analysis of theaggregated transactions.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of public recordsof transactions; associating the transactions with entities; and usingthe aggregated transactions to classify at least one of a supplier and abuyer according to type. In the aspect a buyer may identify like buyers,suppliers like those of the buyer, suppliers of a specified type, orsuppliers like to prefer the buyer. In the aspect, a supplier mayidentify like suppliers, buyers like those of the supplier, or buyers ofa specific type.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of public recordsof transactions; associating the transactions with entities; andassessing whether a buyer has ceased doing business with a supplierbased on the transactional data. In the aspect, the assessment is basedon cycle time between shipments, departure of cycle time from ahistorical average, or based in part on a prediction as to inventoryheld by a buyer.

In another aspect of the invention, methods and systems, such ascomputer implemented methods and systems, include: using a computerimplemented facility to collect and store a plurality of public recordsof transactions; associating the transactions with entities; and usingthe aggregated transactions identify at least one of a supplier of aspecific item sold by a party other than the supplier. In the aspect,the specific item is a commodity.

In the aspect, the identification of supplier is based on region ofinterest, customs data, product category, past shipments, or a number ofshipments. The specific item is a service.

These and other systems, methods, objects, features, and advantages ofthe present invention will be apparent to those skilled in the art fromthe following detailed description of the preferred embodiment and thedrawings. All documents mentioned herein are hereby incorporated intheir entirety by reference.

BRIEF DESCRIPTION OF THE FIGURES

The invention and the following detailed description of certainembodiments thereof may be understood by reference to the followingfigures:

FIG. 1 depicts a report showing an overall rating derived for a set ofsuppliers in a category of products.

FIG. 2 depicts a more detailed report on a supplier with ratings along anumber of dimensions of quality and ratings generated by past buyers whohave worked with the supplier.

FIG. 3 depicts combining non-transaction data with transaction data inthe platform.

FIG. 4 depicts providing an indicator of economic leverage.

FIG. 5 depicts predicting an action based on customs transactions.

FIG. 6 depicts making a recommendation based on customs transactionanalysis.

FIG. 7 depicts a marketing tool for a supplier

FIG. 8 depicts a marketing tool for a buyer

FIG. 9 depicts a flow diagram for an overall analysis methodology forrating suppliers.

FIG. 10 depicts fields that are derived from customs data associatedwith supply transactions.

FIG. 11 depicts a plurality of customs records with details that arerelevant to buyer and supplier identification.

FIG. 12 depicts a user interface for identifying a buyer from one ormore of a plurality of customs data fields.

FIG. 13 depicts mapping variations of buyer names to a primary buyer.

FIG. 14 depicts mapping variations of buyer names to a primary seller.

FIG. 15 depicts how multiple customs transaction records can be used toassess buyer loyalty.

FIG. 16 depicts using transaction data that may be indicative of asupplier's degree of specialization.

FIG. 17 depicts customs data indicative of a supplier's degree ofexperience.

FIG. 18 depicts customs data record fields that may affect a supplier'srating based on the quality of the buyers served by the supplier.

FIG. 19 depicts a summary report showing top suppliers and an overallrating for a category of supplier of a particular product.

FIG. 20 depicts reports showing standout suppliers for a particularproduct, including suppliers with highest customer loyalty and customerswith deepest experience shipping to the buyer's jurisdiction.

FIG. 21 shows a detailed report with ratings of a supplier overall andaccording to various dimensions of quality.

FIGS. 22A and 22B show a breakdown of supplier transaction experiencefor a selected time period.

FIG. 23 shows a breakdown of supplier transaction experience accordingto selected factors.

FIG. 24 shows a breakdown of shipment history broken down by piececount.

FIGS. 25A and 25B show a breakdown of shipment history broken down bymonth.

FIG. 26 shows a search window for searching by country.

FIG. 27 depicts an aggregation search user interface.

FIG. 28 depicts using public transactions for merging records.

FIG. 29 depicts classification of buyers from public records.

FIG. 30 depicts predicting minimum order requirements.

FIG. 31 depicts rating a sub-entity of a supplier.

DETAILED DESCRIPTION

Methods and systems are provided herein for facilitating engagement ofsuppliers; thus, a supplier rating facility may make it easier forcompanies of all sizes to do business across borders by helpingcompanies identify which suppliers they can trust. The suppler ratingfacility approach is to leverage a wide variety of quality data sourcesto rate suppliers around the globe. Behind each rating may be a detailedscorecard that evaluates suppliers along key dimensions. By comparingsupplier scorecards, subscribers may determine which suppliers are rightfor them. In one preferred embodiment the rating system is used to rateapparel suppliers, but it should be understood that suppliers in otherindustries may be rated by the same or similar methods and systems, suchas suppliers of consumer electronics, computer equipment, toys andgames, consumer products, textiles, home goods, food, accessories,computer games, automotive parts, electronic parts and equipment, and awide range of other goods and services, such as BPO, softwaredevelopment, call centers, and the like.

Presently, buyers can access a plurality of supplier directories forinformation about suppliers. However, those directories may only containinformation provided by the suppliers themselves, and on occasion,third-party information on limited subjects, such as relating tocreditworthiness. That information may not be particularly useful inhelping customers to distinguish between good and bad suppliers. Incertain preferred embodiments, the supplier rating facility disclosedherein may facilitate the generation of a plurality of reports thatsupplement or substitute for supplier-provided information, the reportsgenerated by methods and systems disclosed herein and based on a widerange of data sources. In embodiments each supplier may receive a ratingbetween 1 and 100. Behind this rating may be a detailed scorecard, eachcomponent of which being generated by an algorithm that operates on oneor more relevant data sources, and that evaluates suppliers alongdimensions that are important to customers.

A supplier rating facility as contemplated herein may provide buyerswith concrete information about which suppliers are good and whichsuppliers are bad, which are trustworthy and which are not, which areexperienced in a particular area, and the like. The ratings may featurea range of information about suppliers, including analysis generated byalgorithms operating on relevant data sources and, in certain optionalembodiments, ratings from previous customers. Analysis may include,among other things, using publicly available but currently fragmentedinformation. In various embodiments, the supplier rating facility mayrate suppliers along several dimensions, including without limitationamount of international experience, degree of specialization, andstandards compliance.

In certain optional embodiments, ratings from previous customers mayenable suppliers to gather and showcase feedback from their previouscustomers. Buyers may pay a subscription fee for access to ratingsdetail. Existing business-to-business sites may be able to embed thesupplier rating facility in their directories and benefit from newrevenue streams. Although apparel is being used as an embodiment of theinvention, it should be understood that the invention may be applied toany industry, such as furniture, electronics, textiles, chemicals, toys,food, and the like. In addition, services in addition to a ratingsservice may be facilitated through the invention, such as for billing,transactional settlement, insurance, social networking for buyers, andthe like. In embodiments, the invention may be applied to a broadspectrum of industries where buyers and sellers are located acrossdiverse environments, and supplier-product information and ratings arefragmented.

The supplier rating facility may provide a ratings platform wherebuyers/suppliers may compare and contrast potential suppliers/buyers.The ratings platform may generate and maintain ratings of suppliers,buyers, countries, geographic regions, marketplaces, commodities and thelike. The ratings may be presented in various forms including a listingof supplier ratings as shown in FIG. 1. The supplier rating list 100 inFIG. 1 includes a keyword 102 around which the list is based. Althoughone keyword is shown in FIG. 1, a keyword phrase, group of keywords,logical combination of keywords, and the like may be used as a basis forthe list 100. In an interactive embodiment of the list of FIG. 1selecting the keyword 102 (e.g. knitting) may allow a user to makechanges to the keyword 102 to present a revised list 100. Also in aninteractive embodiment of the list of FIG. 1, a menu 104 may be providedto facilitate access to other aspect of the platform and to one or morewebpages associated with the platform. The list 100 may include anynumber of suppliers that satisfy the keyword 102 criteria; in theexample of FIG. 1 the list includes 10 suppliers. The number ofsuppliers presented may be limited to fewer than the total number thatmatch the keyword 102 criteria. Aspect of the list 100, such as a limiton the number of supplies in the list 100 may be controlled bypreferences (e.g. user, platform, supplier, and the like).

The list 100 may include entries 108 for each supplier that satisfiesthe keyword 102 criteria. An entry 108 may include an overall rating 110also known as the “Panjiva Rating”, the supplier name 112, selectedbibliographic data 114, and the like. Preferences as indicated above mayimpact what information is presented in an entry 108 and the embodimentof FIG. 1 is only an example of one set of information to be presented.Each supplier may be given an overall rating 110 that may be based on a100 point scale so that an overall rating 110 may be between one andone-hundred as shown in FIG. 1.

An alternate view of supplier rating is exemplified in FIG. 2 whichdepicts a supplier scorecard 200, which may be a detailed view of asupplier aspects related to the overall rating 110. The scorecard 200enhance the overall rating 110 by providing details about the overallrating 110. A comparative rating 202 may show the supplier overallrating 110 in light of an average of other suppliers and may include anindication of a confidence in the overall rating 110. This scorecard 200may assess the supplier's relative strength along a variety ofdimensions. Leveraging a wide variety of data sources, the supplierrating facility may rate suppliers along key dimensions 204 also knownas “Panjiva Analysis” ratings, in a plurality of categories such asbusiness basics, international track record, certifications, and thelike. The rating platform may also allow buyers to rate suppliers alongseveral dimensions. The scorecard 200 may include the buyer ratings 208.In embodiments, the ratings platform may become a place where buyers goto hold suppliers accountable. In embodiments, reports such as thescorecard 200 may be made available to users in online and print forms.

In embodiments, a backend infrastructure may automatically generatecustomized documents by programmatically generating a representation ofthe document in a typesetting language such as Tex, LaTeX, and the like,which may then be processed and turned into a PDF document.

The “Business Basics” section of the Supplier Scorecard 200 may help abuyer assess whether a company is legitimate and worthy of considerationas a potential partner. Included in “Business Basics” may be informationon whether a company has registered with authorities, as well as anassessment of a trading environment in the supplier's country, combiningmacro contextual information with data that is specific to an individualsupplier and the like. A facility for determining a track record in aparticular jurisdiction may use government and third-party data, and mayassess the amount of experience a supplier has serving thatjurisdiction, and the loyalty that a supplier's customers havedemonstrated, and the like. The “Standards Compliance” section of theSupplier Scorecard 200 may document whether a supplier has beencertified as meeting international standards for quality management,respect for the environment, social responsibility, product safety, andthe like.

The ratings scorecard 200 may include a plurality of analysisdimensions, such as county context, business legitimacy, publicrecognition, amount of experience, caliber of customers, customerloyalty, specialization, quality management, social responsibility,environmental responsibility, and the like. Buyer feedback dimensionsmay include product quality, customer service, timeliness of delivery,language skills, sample-making ability, respect for intellectualproperty, and the like. Supplier information may include contactinformation, areas of expertise, caliber of customers, ratings, and thelike. A confidence in buyer feedback may be established by determiningthat the feedback is being provided by a supplier who is or recently wasreceiving shipments from the supplier. This can be done by ensuring thattransaction records validate that at least some supplier shipments weresupplied to the buyer providing the feedback. In embodiments,information utilized in the formation of the ratings scorecard 200 maybe from shipment history, such as frequency, quantity, and the like;shipment capacity estimation, which may be based on shipment data asopposed to information provided by the supplier.

Contact information may include making all contact information availableto subscribers, so that they may directly contact suppliers. Areas ofexpertise may tell a buyer which products a supplier has shipped, whichmaterials it has used, which techniques it has employed, and whether ithas produced men's apparel, women's apparel, or both. Caliber ofcustomers may tell a buyer which types of customers a supplier hasserved, such as premium, mass, discount, and/or niche customers. Inembodiments buyers may rate suppliers with whom they have done business.After a buyer rates a supplier, the supplier rating facility may verifythe two have actually done business together, such as by identifying acorresponding customs records that shows an actual import transaction inwhich the buyer imported goods from the supplier, from a bill of lading,from a bank-issued receipt, and the like. Thus, methods and systemsdisclosed herein include methods and systems for deterring fraudulentratings by verifying the existence of the transaction purportedly ratedby the buyer. This may prevent false ratings that are either toopositive (such as by an affiliate or cohort of the supplier) or toonegative (such as by a competing supplier posing as a buyer). Afterverification, the buyer's rating may become part of the supplier'sscorecard. As part of the verification process, the buyer's identity maybe revealed to the supplier. However, in embodiments the buyer'sidentity may be obscured so that it does not appear on the supplierrating facility's website and is not shared with anyone else. Inembodiments buyer feedback may only be viewed by buyers who haveprovided feedback on their suppliers. In embodiments, a computerfacility for recording transactions associated with one or more buyerswith one or more sellers may include a user interface that mayfacilitate determination of entity score based on transactional data.The transactional data may be related to the shipping details of thegoods and services associated with different entities. In an example,entities such as buyers may order goods and services from the sellersresulting in transactions. An aggregation facility may collect, combineor aggregate transactions associated with different entities.Subsequently, an association facility may facilitate association oftransactions with different entities. The transactions may be analyzedby the analysis facility to generate an entity score corresponding toeach entity.

In embodiments, rating a supplier, buyer, or other entity may result ina score that is at least partially based on predefined criteria, such asa user provided criteria. Alternatively, the system and methods hereinmay facilitate rating of a supplier, buyer, or other entity based one ormore algorithms. The rating algorithms may be manually selected, or maybe selected automatically based on a set of algorithm selection rules.In an example, a supplier may be known in the industry as highlycredible. One or more rating algorithms may be applied to transactiondata and may use predefined criteria for the algorithms tomathematically determine the credibility of the supplier. Thisdetermined credibility rating may be provided to the buyer through auser interface of the platform.

In embodiments, the entity score may be based in part on transactionaldata related to the shipments by the entity such as delivery data,amount shipped, location of shipment and the like. In an example, asupplier providing goods and services within the stipulated deliverydate may garner higher ratings compared to the suppliers who failed todeliver on time.

In embodiments, the entity score may be based on one more factorsincluding country context, business legitimacy information, publicrecognition, amount of experience, caliber of customers of the supplier,customer loyalty for the supplier, degree of specialization of thesupplier, and feedback from previous customers or some other factors.Further, each factor or group of factors may include a list ofparameters. A user interface may be configured to allow a user to selectsome or all the parameters from this group to generate an entity rating.In an example, the group country content may include variables such asGNI per capita, currency volatility, cost to export, politicalstability, and the like. The user interface may allow a user to selectGNI per capita and cost to export to generate a country context valuethat would be applied to calculate an entity rating. Furthermore,determination of the entity score may depend partially or completely onsome or all the parameters selected from some or all the groups, asdescribed herein and elsewhere. In an example, a buyer who may beinterested in knowing the quality of a product or service provided by asupplier may select feedback from previous customer groups on which tobase an entity score for the supplier. This group may further includeparameters such as timely delivery of goods, quality of goods, number oftransactions and the like. Rather than choosing just the group rating todetermine an entity rating, the buyer may choose some or all theparameters from this group to determine the entity score associated withthe supplier. In another example, the score associated with the suppliermay be determined based on two or more groups comprising multipleparameters, such as the group pertaining to the degree of specializationof the supplier and the group pertaining to feedback from previouscustomers. The buyer may select the group degree of specialization andone or more parameters from the group. Similarly, the buyer may selectone or more parameters from the feedback from the previous customersgroup. The entity score may be determined based on parameters selectedin each of the groups.

A user interface, such as the user interface of FIG. 2 may be used topresent the various ratings, scores, and rating factors to be applied tothe entity rating score.

In embodiments, a supplier rating facility or buyer rating facility maytake ratings along each of key dimensions, weight the ratings to accountfor the fact that some dimensions are more important than others,calculate an overall rating 110, and the like. In embodiments, ratingsmay provide a measure of caliber, such as the caliber of a buyer or thecaliber of a supplier.

The supplier rating facility may rate suppliers across a plurality ofdifferent dimensions, some of which may derive from actual transactionaldata, such as customs data, with others based on sources such as Dun &Bradstreet, the World Bank, auditing firms for various certifications,government sources, and the like. In embodiments, more weight may begiven to recent data, data for larger transactions, data for higherquality buyers, or other types of data with respect to which there is anindicator that the data may have higher relevance than other types ofdata. The supplier rating facility may also provide a more intuitiveunderstanding to ratings, by considering caliber of customers, customerloyalty, specialization, and the like. Caliber of customers may involvemanually grouping buyers into distinct bands or tiers, such as premium,mass-market, discount, niche, and the like and then computing a sumbased on the newness of each buyer-supplier relationship and the tier ofeach buyer.

In embodiments, rating for a supplier may be based on the aggregatedtransactional customs data, a user defined criteria, customs datarelated to transactions by the supplier with a third party or on someother parameters. In an example, a supplier may be rated based on thenumber of transactions done with a particular buyer. In another example,a user may define adherence to delivery data as a criteria for ratingthe supplier. In addition, the rating of the supplier may be based atleast in part on loyalty as indicated by an analysis of customstransactions. Furthermore, the determination of rating for the suppliermay based on an amount of supplier experience as indicated by customstransactions related to the number of shipments, duration of supplierexperience as indicated by shipments, size of transactions as indicatedby past shipments, extent of international experience as indicated bypast shipments, extent of country-relevant experience as indicated bypast shipments and the like.

In embodiments, a rating for a buyer may be based on aggregatedtransactional customs data, customs data related to transactions of thebuyer with a third party or some other parameter. In an example, ratingsfor the buyer may be based on the feedback about the buyer provided byone or more suppliers. In addition, ratings for the buyer may also bebased on two or more factors selected from a group including the countrycontext of a party, the business legitimacy of a party, whether a partyis registered with government authorities, an assessment of a tradingenvironment in a country, macroeconomic information, public recognitionof a party, industry awards, industry certifications, amount ofexperience, number of shipments, duration of experience, size oftransactions, extent of domestic experience, extent of internationalexperience, caliber of customers, customer loyalty, degree ofspecialization, specialization in product categories, specialization inmanufacturing techniques, specialization in materials, specialization ingender, feedback from customers, feedback from buyers, feedback onproduct quality, feedback on customer service, feedback on timeliness ofdelivery, feedback on language skills, feedback on sample makingability, respect for intellectual property, quality management, socialresponsibility, environmental responsibility, standards of compliance,certifications, and certifications with respect to specific vendorstandards and the like.

In embodiments, the rating of the buyer may be based on loyalty asindicated by an analysis of customs transactions. In an example, a buyermay be rated based on the number of transactions with a particularsupplier in a specific time frame. In addition, the buyer may be ratedbased on an amount of experience as indicated by customs transactionsrelated to the number of shipments, duration of experience as indicatedby shipments, size of transactions as indicated by past shipments,extent of international experience as indicated by past shipments,extent of country-relevant experience as indicated by past shipments andthe like.

In embodiments, a rating may be made of customer loyalty for a supplier.A customer loyalty rating method may include analyzing the set of buyerswho have done business with each supplier over the course of severalyears, and identifying ‘loyalty periods,’ intervals in which a buyerconsistently sources from a given supplier, and ‘switches,’ where abuyer ceases obtaining a given set of products from one supplier andbegins sourcing from another supplier. Suppliers for whom there havebeen many switches may be given lower ratings, while suppliers with longloyalty periods and few switches may be given higher ratings.

In embodiments, a rating may relate to the degree of specialization of asupplier. A specialization rating method may decompose suppliershipments into dimensions, such as product category, technique,material, gender, and the like. These dimensions may be independent ofthe rating dimensions or may be used as a factor in rating.

In embodiments, methods and systems may include methods for generatingraw scores. Generation of raw scores may use a variety of techniques totransform raw customs data and other third-party data into meaningfulratings. Consideration may be given to customer loyalty, caliber ofcustomers, amount of experience, specialization, country context,business legitimacy, environmental responsibility, socialresponsibility, quality management, public recognition, and the like.Customer loyalty rating may include identifying shipping patterns, buyerpatterns, loyalty periods, and the like. Caliber of customer ratings mayinclude assigning a buyer tier, length of time in that tier, age ofbuyer, and the like. Experience ratings may include evaluating number ofshipments, duration of experience, size of transactions handled, and thelike. Specialization ratings may include or reference a measure of theextent to which a supplier focuses on a narrow range of products,materials, and/or techniques. Business legitimacy ratings may beprovided by a supplier having government registration records, a Dun &Bradstreet DUNS number, or other evidence of business legitimacy.Environmental, social, product safety, and quality management ratingsmay be derived from a supplier having appropriate certifications, andthe like. Public recognition ratings may include reference to governmentand industry awards, and the like. In embodiments, high risk suppliersand high risk buyers may be identified, such as in association withindividuals and organizations that work with high risk suppliers andhigh risk buyers. Country context ratings may be related to the countryin which a supplier is located, as well as data supplied by the WorldBank, International Monetary Fund, and other sources, about thatcountry. Other sources may include GNI per capita, currency volatility,cost to export, political stability, credit rank_, export_cost,gci_efficiency_enhancers, and the like. A country context computationmay include calculating a weighted sum of log(gni_per capita),credit_rank, log(export_cost) and gci_efficiency_enhancers that may bethen thresholded into final score buckets. The weights and thresholdsused in the country context computation may have been determined usingmachine learning techniques (e.g. decision trees and principal componentanalysis) to determine the relevant features to weight, appropriateweights, and effective thresholds.

Generating ratings from raw scores may include a weighting,standardization or normalization factor applied to raw data to produce astandard score that may be centered on zero, and then normalized to arating between 0 and 100. These values may then be applied to ascorecard 200 that presents the normalized data for a supplier. Inembodiments, the ratings may be scaled linearly to provide a mean ofapproximately 50, as in a Gaussian distribution.

In addition, ratings may be customized to individual buyer preferences,such as by having buyer's rate suppliers with whom they have donebusiness. Ratings may then be tuned to best match this empirical view ofa buyer's preferences. Such an approach may use a machine learningtechnique such as a support vector machine. Over time, trends in ratingsmay then be captured and displayed to the buyer. Such trends may enablea graph-theory analysis (e.g., minimum cut, maximum flow, cliques, andthe like) on buyer-supplier networks to determine the relationshipsbetween groups of buyers and suppliers, which may lead to additionalvalue-added services such as improving production allocation for buyers.

Referring to FIG. 3, integration of transactional data with data fromnon-transactional data sources is shown. A computer facility 302 mayreceive transaction records associated with a plurality of buyers 318such as 318A and 318B and/or a plurality of sellers 320 such as 320A and320B. Furthermore, the computer facility 302 may include an aggregationfacility 304, an association facility 308, a storage facility 310, anintegration facility 312, an analysis facility 314, and the like. Theaggregation facility 304 may collect and combine the transaction recordsassociated with buyers 318 and suppliers 320 for processing at theassociation facility 308. The association facility 308 may enableassociation of transactions with different entities such as buyer 318Aand supplier 320B. The association facility 308 may be coupled to any ofthe other facilities within the computer facility 302, such as theintegration facility 312 which may receive non-transaction data fromnon-transaction sources 318. The analysis facility 314 may facilitateevaluation of suppliers 320 and buyers 318 based on the data integratedfrom other sources 318 and the data received from the associationfacility 308.

In embodiments, public records may include customs records apart fromother records. The customs record may include information associatedwith an entity as captured by a customs organization. The informationmay be useful in identifying the different transactions associated withthe entity's billing based on the entity's custom identification number.

Data sources may leverage data from several hundred data sources, suchas, International Oeko-Tex Association, Social AccountabilityInternational, Worldwide Responsible Apparel Production (WRAP),Fifty-five ISO 9001 auditing firms, Forty-six ISO 14001 auditing firms,Forty-seven OHSAS 18001 auditing firms, Two GB/T 18885 auditing firms,United States Department of Homeland Security, Ministry of Commerce ofthe People's Republic of China, General Administration of Customs of thePeople's Republic of China, and the like. In embodiments, custom datamay be from countries all over the world, covering exports and imports,where an import record may be matched up with an export record.

Tools used in the analysis of supplier and buyer data may include amerger tool, a suggestive merger tool, a buyer caliber tool, a buyermarketing tool, a name chooser tool, a country manager, an API, aproduct keyword manager tool, a statistics tool, a report generationtool, a supplier marketing tool, a name updater, and the like. Any ofthese tools may be embodied in the facilities of computer facility 302.

In embodiments, aggregated customs data may be processed to identifytransactions associated with different types of entities such as buyersand/or suppliers. In addition, based on the transactions associated withdifferent entities, an entity type may be determined from one or moreentity types present in the transactions. In an example, an entity maysupply woolen clothes and the transactions associated with the shipmentsof the woolen clothes may be recorded as being provided by ‘ABC co’. Inanother transaction, the same entity may be recorded as ‘ABC Company’.This variation may be due to a difference in recording of transactionsof customs data due to variations in filling data in the customs formrather than the entities being different entities. A data mergingfacility may allow automatic merging of transaction records describedabove under a single entity based on the inference made on grounds ofsimilarity of data. In this example, the variations ‘ABC co’ and ‘ABCCompany’ may form a valid case of merging of data based on the minorvariation in entity name. Alternatively to automatic merging, or inaddition to it, suggestions for merging similar data based on similarityin data elements may be provided to a user. In the above example,records relating to transactions ‘ABC co’ and ‘ABC Company’ may bepresented to the user with a suggestion to merge them under a singleentity based on similarity of data elements. The similarity of dataelement in the records may be determined by the data merging facility.Based on the user's response, automatic merging of the two entity namesmay be learned by the platform.

An entity may be associated with one or more names for performingtransactions that may be captured in customs records. As describedabove, the difference may be due to a variation in recording of thecustoms data. The systems and methods described herein may facilitatemerging of any number of transactions that should be associated with aparticular entity even though the records show a plurality of similarbut varied entity names.

In addition to facilitating processing aggregated customs data so as toassociate a set of transactions with an entity, a plurality oftransactions that are properly associated with a plurality of entitiesmay be merged under an entity type for purposes of evaluating thetransactions and entities associated therewith. The merged records maybe useful in evaluating a market segment, consortium of companies,industry segment, regional results, class of entities, and the like. Inan example, transactions associated with several entities may be mergedbased on the basis of the transactions being associated with a singlebuyer. Even though the transactions call out different suppliers indifferent industries, the single buyer is a basis for processing thetransactions as if they were merged.

An entity type may be defined based on any aspect of an entity that maybe used to process customs transaction records. The methods and systemsof filtering, classification, and clustering of transactions asdescribed herein may be applied to identify transactions that aremergeable under an entity type. In an example, a buyer may initiatepurchase transactions with four suppliers of components to produce anitem. The transactions between any of the four suppliers and the commonbuyer can be merged (or tagged as mergeable) as having a common entitytype such as “supplier to common buyer”. Other suppliers who ship itemsto the common buyer may have their transactions with the common buyermerged under the same entity type.

In many cases multiple data records exist for a single supplier, but therelationship of those records to that single supplier is ambiguous. Inan example, the name of the supplier might appear in one field in onerecord, but in a completely different field in another record. This isoften the case in customs data, where forms are filled out withinformation in various fields, notwithstanding the purportedstandardization of the forms. In embodiments, a merger tool may be usedto merge data records of two apparent suppliers that should really beone supplier. The merger tool may evaluate an address, and if the samein two records, select a parent record and identify child records, uponwhich records are merged into a single record. In embodiments, a mergertool may merge records that are on the same page or more generally mergerecords across a database. In embodiments, a merger tool may use apattern matching technique to identify potential candidates for mergingof records

As shown in FIG. 4, an indicator of economic leverage may be provided.The economic leverage may be based on an analysis of customstransactions data. The indication of the economic leverage may beprovided by an indication facility 412 of the computer facility 402. Thecomputer facility 402 may also include a collection facility 414, astorage facility 410, an aggregation facility 404, an associationfacility 408, and an indication facility 412. The collection facility414 may collect a plurality of records of customs transactions of aplurality of buyers 418. In addition, the collection facility 414 maycollect a plurality of records of customs transactions of a plurality ofsuppliers 424. In an example, the collection facility 414 may collectthe record of customs transactions of buyer 420 and buyer 422. Inaddition, the collection facility 424 may collect the record of customstransactions of supplier 428 and supplier 430. The storage facility 410may store the plurality of records of customs transactions of theplurality of suppliers 424 and the plurality of buyers 418. Theaggregation facility 404 may aggregate the transactions. The associationfacility 408 may associate the transactions of the plurality ofsuppliers 424 and plurality of buyers 418. The association facility 408may associate the transactions with entities. The entities may include,but may not be limited to, companies, buyers, sellers, suppliers,distributors, factories, subsidiaries of a supplier and the like. Ananalysis facility 432 may analyze the aggregated transactions. Theindication facility 412 may provide an indication of economic leveragewith respect to an entity based on an analysis of the aggregatedtransactions. In an example, the indication facility 412 may indicate tothe buyer 420 that it would be economical to buy 40 tons of silk fabricfrom the supplier 428. Similarly, other economic indicators may beprovided to the plurality of buyers 418 or the plurality of suppliers424.

In embodiments, the indicator of economic leverage may be with respectto the supplier 428 based on transactional customs data for the supplier428. In embodiments, the indicator of economic leverage may be withrespect to a supplier 428 based on transactional customs data for aparty other than the supplier 428. In embodiments, the indicator ofeconomic leverage may be with respect to a buyer 420 based ontransactional customs data for the buyer 420. In embodiments, theindicator 420 of economic leverage may be with respect to a buyer 420based on transactional customs data for a party other than the buyer420.

In embodiments, as shown in FIG. 5, a prediction facility 502 maypredict an action of an entity. The action may be based on the analysisof the aggregated transactions. The prediction may relate to whether thesupplier 428 will work with the buyer 420 of a given size. Theprediction may also relate to whether the supplier 428 will work withorders of a given size.

In embodiments, the prediction may be of an action of the buyer 420based on an analysis of customs data for the buyer 420 transactions. Theprediction may be related to a price, a change in price, a change insupplier, a quantity ordered by the buyer 420 and the like. Inembodiments, the prediction may be of a buyer action of based on ananalysis of customs data for transactions by a party other than thebuyer 420. In embodiments, the prediction may be of a supplier actionbased on an analysis of customs data for transactions by the buyer 420.In embodiments, the prediction may be of a supplier action based on ananalysis of customs data for transactions by a party other than thebuyer 420. In embodiments, the prediction may be related to a potentialclosure. The closure may be of a subsidiary, a factory, a company andthe like. Those skilled in the art would appreciate that the predictionfacility 502 may provide the predictions to the plurality of buyers 418,plurality of suppliers 424 or some other entities.

In embodiments, as shown in FIG. 6, a recommendation facility 602 mayprovide recommendations based on analysis of customs transactions. In anexample, the recommendation facility 602 may recommend to the buyer 420to buy 40 tons of silk fabric at a discounted price from the supplier428 based on transaction records indicating that the supplier hasreceived returns of the silk from buyers. Similar recommendations may beprovided to the plurality of buyers 418 and to the plurality ofsuppliers 424.

In embodiments, the recommendation may be based on analysis of customsdata for the buyer 420 transactions. In embodiments, the recommendationmay be based on analysis of customs data for transactions by a partyother than the buyer 420. In embodiments, the recommendation may bebased on analysis of customs data for transactions by the buyer 420. Inembodiments, the recommendation may be based on analysis of customs datafor transactions by a party other than the buyer 420.

In embodiments, the recommendation may be based on prioritization offactors by a user. In an example, the buyer 420 may require 40 tons ofsilk within 4 days. The recommendation facility 602 may recommend buying40 tons of silk from supplier 430 based on its manufacturing capacity of50 tons per day and ability to provide the required silk within thestipulated time. In embodiments, the recommendation may be based on auser-specified rating factor.

In embodiments, a suggestive merger tool may use more sophisticatedtechniques to suggest which buyers or suppliers should be mergedtogether, such as the supplier in question listed, then producingpotential matches. Such techniques may use text similarity metrics onthe name and the address and performing algorithmic steps such assorting the tokens by alphabetical order, so word transpositions do notchange lexical distance of names in pattern matching. Such techniquesmay determine how each word in a given buyer or supplier's name orbuyer's name contributes to the uniqueness of the name, and uses thisinformation to make relevant suggestions for merging. In embodiments, asuggestive merger tool may use a machine learning approach to performpattern matching or otherwise suggest merger of records, such as atechnique with boosted trees or other machine learning techniques.

A buyer caliber rating may be assigned manually by checking a box basedon a facilitator's or host's assessment of the caliber of the buyer, orby automated techniques. In embodiments, a search link may be providedfor each buyer, such as one that retrieves search results from a searchengine, directory, rating system or other source of information aboutthe buyer. In embodiments, an interface, such as an overall buyermanager, may assist suppliers in searching for different buyers.

In embodiments, a buyer marketing tool may break down data for aparticular supplier, such as addresses (from customs data), raw customsrecords, records that show customer loyalty periods and switches toother suppliers, specific breakdowns of what the supplier has shipped(e.g. in terms of product category, material, technique, gender of theshipped garments, and the like), breakdowns of the size of the shipmentsthe supplier has made, breakdowns of the number of shipments that thesupplier has made each month over some time period, as to determine theestimated capacity of a supplier, and the estimated minimum shipmentthat a supplier is willing to produce. In an example, a tool may show abreakdown of suppliers (e.g. showing a number of suppliers, such as 35suppliers in ratings for each), where it is possible to see a history ofwhich suppliers buyers have used. This may allow marketers to evaluatetheir performance relative to other suppliers with whom they compete.

A country manager tool may be used to add data on countries (such as forthe country context dimension of an overall rating 110 or analysis).

In embodiments, an application programming interface may be provided forthe platform described herein, whereby other computer programs mayaccess the reports generated by the platform, such as accessing overallratings 110, specific components of ratings, results of particularalgorithms, or data sources used in the platform. Thus, other partiesthat engage in global trade, such as clients of the facilitator andpartners, may obtain access to the platform, allowing the ratingsmanaged by the platform to become a standard measure by which suppliersare rated.

A product keyword manager tool may provide an ontology or hierarchy fora search interface, such as using graphs, charts, and the like. Themanager may allow a facilitator to add or delete sub-categories to acategory. Keywords associated with each of the categories may be usefulfor: (1) getting the data (allowing a user to scan through raw text ofcustoms data, looking for these keywords, which is one way for thefacilitator to know that a supplier has shipped something within acategory; In an example, a search for infant clothing might search forall sub-categories, using words such as baby, infant, kiddy, kid,layette, maternity, newborn, toddler, and the like); and (2) usingkeywords for text entry into the search field (such as synonyms to getbetter search results). In an example, in a hierarchy of materials,there are sub-materials of materials, and each has keywords associatedwith it. In embodiments, a facilitator may engage in a process (manualor automatic) to generate key words, such as using glossaries that listall of the products and materials, with specific definitions. Inembodiments, algorithms may be used to determine the market vertical(e.g., apparel supplier or electronics supplier, etc.) of a supplierbased on the aggregate contents of all of its shipments. In embodiments,customs records may be utilized to identify what industry or verticalthe material is in.

A statistics tool may assist in providing distributions of data. Thus, afacilitator may support statistical distributions for all dimensions ofdata analyzed by the methods and systems of the platform.

A bucket boundary check tool may assist in testing that suppliers thatfall within specific rating “buckets” or bins (e.g., Excellent,Unproven, etc.) by showing suppliers that are at the high and lowboundary of each bucket.

A report generation tool may automatically build PDF reports or reportsin other output formats, such as PowerPoint, Excel, Word, and the like.The report generation tool can be used as an administrative tool, or asa tool to allow users or clients to build custom reports, such as onesthat incorporate some or all of the data generated by the platform. Inan example, a user may specify which supplier or suppliers the reportshould include. A report could be made vertical-specific, covering anumber of suppliers in a vertical, such as a theme or characteristicgrouping, or it could relate to standout suppliers (such as ones withhigh customer loyalty ratings, top amounts of experience, or the like).In embodiments, a user may turn on or off various sections. Inembodiments, the end product of a report may be a link that allows theuser to download the report (in PDF format or some other format) orwhich sends the report via email (in PDF format or some other format).

In embodiments, a buyer marketing tool may provide information aboutwhat product materials, product techniques, and the like particularbuyers require from their suppliers. Such a tool may also provideinformation relating to how many shipments particular buyers importedover time, as well as the breakdown of shipment sizes.

A marketing tool may be used by an operator of the platform to at leastidentify opportunities of marketing the products and services associatedwith the platform to suppliers, buyers, and others. The marketing toolcan also be accessed by suppliers as shown in FIG. 7 and by buyers asdepicted in FIG. 8. However, when used by the operator or owner of theplatform or of an implementation of a portion of the platform, themarketing tool has significant capabilities. The marketing tool may workcollaboratively with other elements of the platform, such as elementsthat perform aggregation, association, merging, storage, collection,analysis, user interface and the like. A marketing tool may be used toidentify instances of potential scenarios (e.g. suppliers in financialdistress) to offer entities that may be potentially impacted by thescenario instance (e.g. the supplier's shippers, buyers, suppliers ofraw goods, and the like) with services and products available throughthe methods and systems described herein.

The marketing tool may also work cooperatively with a user interface tofacilitate an operator entering parameters of marketing opportunityscenarios that the marketing tool can evaluate. The entered scenarioparameters and attributes may be applied to an analysis of customstransaction data and marketing opportunities may be presented to theoperator through the user interface.

As shown in FIG. 7, a marketing tool 712 may be provided for a supplier728. The marketing tool 712 may be provided in a computer facility 702.As explained in the description for FIGS. 4, 5 and 6, the computerfacility 702 may include a collection facility 714, a storage facility710, an aggregation facility 704, and an association facility 708. Thecollection facility 714 may collect a plurality of public records oftransactions among a plurality of buyers 718 and a plurality ofsuppliers 724. In an example, the collection facility 714 may collectthe public records of transactions between buyer 720 and supplier 722.The storage facility 710 may store the plurality of public records oftransactions among a plurality of buyers 718 and a plurality ofsuppliers 724. The aggregation facility 704 may aggregate thetransactions. The association facility 708 may associate thetransactions with various entities that may include, but may not belimited to companies, buyers, sellers, suppliers, distributors,factories, subsidiaries of a supplier and the like. An analysis facility732 may analyze the aggregated transactions. The marketing tool 712 maysuggest a marketing strategy for the supplier 728 based on analysis oftransactional data from public records. In an example, the marketingtool 712 may suggest to the supplier 728 that it would be lucrative tosell 100 tons of silk fabric every week to the buyer 720 located in theUnited States of America. Those skilled in the art would appreciate thatthe marketing tool 712 may suggest marketing strategies to a pluralityof suppliers 724 simultaneously.

As shown in FIG. 8, a marketing tool 802 for the buyer 720 may beprovided. The marketing tool 802 may be provided in a computer facility702. The marketing tool 802 may suggest a marketing strategy for thebuyer 720 based on analysis of transactional data from public records.In an example, the marketing tool 802 may suggest to the buyer 720 thatit would be lucrative to buy 50 tons of silk fabric on a monthly basisfrom the supplier 728 located in China. Those skilled in the art wouldappreciate that the marketing tool 802 may suggest marketing strategiesto a plurality of buyers 718 simultaneously.

Processing customs transactions and other records may involve amulti-step method. Data from customs organizations, such as US Customsmay be provided on a removable computer memory such as a CD, DVD, flashmemory, memory stick, USB memory card, and any other type of removableor portable memory device. Alternatively the customs data may beacquired via a network, such as the Internet, a dialup connection, avirtual private network, a dedicated network, and the like. The data mayalso be converted from a proprietary format for further processing bythe platform. Each customs organization, and within any customsorganization for a particular country may have a different format orstorage device for records. Conversion may be performed on the data sothat the end result is independent of the physical format of deliveryand the logical formatting of the information. In this way data in asubstantially unified format may be processed by the methods and systemsof supplier rating and the like that are herein disclosed. In anexample, US customs data may be provided on a CD and may be in a COBOLformat. The data on the CD may be retrieved and automatically loaded toa server. The server or another computing device may convert the datafrom the COBOL format to an XML format. The XML formatted data may beloaded to a database such as a Postgres database for further processing.In this example XML format represents a unified format for the customsdata.

Processing the converted transaction data may include multiple steps ofdata analysis in which a confidence level may be applied. Confidencelevels may be grouped into confidences bands that may help target eachtransaction toward one of merging (high confidence band), suggesthuman-aided merging (medium confidence band) and do not merge (low orlacking confidence band). Analysis of the transaction data may revealimportant information about the entities involved in the transactions.In an example, a single entity may appear as a buyer in one transaction,a shipper in another and a supplier in a third. Ensuring that eachtransaction is properly associated with the entity as its intendedfunction (buyer, shipper, and supplier) may be accomplished throughvarious analysis and assessment techniques including, similarityassessment, filtering, classification, clustering, and the like.

Processing of customs data may also include text mining. Text mining mayinclude searching for key words, terms, or phrases that are known,predetermined, or specified for the mining operation. An ontology ofterms, such as ‘gender dyeing’ may be applied in text mining. Inaddition, synonyms of keywords may be mined. Text mining alsofacilitates populating reports with various data, such as time seriesdata of shipments per month and weight of shipments per month.

Data may be further analyzed with a monitoring tool that may look foranomalies, such as peaks, and other statistical measures to identifypotentially important events that are captured in the transactions.Analyzing data for peaks and the like may help with activating buyers,suppliers, shippers, and other entities for use in the platform. Astatistical event, such as a spike in orders by a buyer that otherwisehad little transaction history may trigger an indication that the buyershould be activated for use in ratings of buyers, suppliers, and thelike. Alternatively, or in addition, an entity may be activated based onthe transactions for the entity complying with criteria such as ashipment quantity threshold, and the like.

Merging data ensures that all records of transactions associated with anentity (buyer, supplier, or the like) are properly recorded against thecorrect entity. However, due to the large number of data sources,substantial variations in how an entity may be identified in recordsfrom the data sources, parent-subsidiary entity relationships,transaction system limitations (e.g. limiting the number of charactersin an entity name), regional differences, dialect differences, use ofshort hand for entity information, various coding schemes used bybuyers, suppliers, and the like proper merging of data is complex anddifficult.

In a fundamental example, merging is taking two records for the sameentity that each has entity information that varies substantially onefrom the other and ensuring that the records are properly recordedagainst the one entity rather than being assigned to two separateentities. A merger tool may provide robust, accurate, and efficientmerging of data by resolving the variations, some of which are describedabove so that records for a single entity are merged, while ensuringthat records for a different entity are kept separate from the singleentity.

Within any given country, industry, region, or language there is nouniversal entity identifier that could be applied to the data records touniquely identify which entity is associated with each transactionrecord. Also, with data records being provided by sources from manycountries, in many industries, and across many languages the mergingchallenge is increased. A way to meet this challenge today is to performprocessing of the text that is present in the records to determine whichrecords should be merged under an entity. Various techniques of textassociation, filtering, character grouping, thesaurus lookup, machinelearning, natural language processing, search-based comparison,classification, known entity matching, clustering, and the like may beapplied to identify mergable records. The complexity and challengepresent in merging may require applying each technique in an intelligentway so that highly computation intensive processes such asclassification are used appropriately.

One objective of merging is to take a set of input records and matchthem to entities that are already known to the platform, such asentities already included in an entity database or other database of theplatform. When a match cannot be determined automatically with asufficient confidence level, then information may be presented to anoperator or user of the platform to make a final determination of theentity associated with the record(s).

The methods and techniques for identifying mergable records may beprogrammed into a processing unit and run in a sequence that facilitatesrapid and robust merging of records. At least three types of processingmay be performed on records for merging assessment: filtering,classification, and clustering. Each processing type will be describednow.

Because classification and clustering may be very expensive in terms ofcompute/processing time, filtering is applied to distinguish candidaterecords for classification from records that are unlikely to bemergeable under a single entity. Filtering provides various techniquesto help identify only the records that classification may have anychance of merging. Filtering for the purposes of merging may beconsidered a coarse sort of the records, capturing candidates forclassification and passing through those records that appear to be farremoved from the captured records. Filtering may be performed by avariety of filter-type algorithms. In one example filtering may beperformed by search engine software, such as the open source lucenesearch engine applied. In another example of filtering, sometimesreferred to as “kgram filtering”, several small consecutive strings ofcharacters are captured from each of two records and compared. Kgramfiltering may be based on techniques of dynamic programming. In anapplication of kgram filtering, when a sufficient number of thecharacter strings match between the records, the records may beidentified as potential candidates for further processing such asclassification and clustering. One benefit to kgram filtering is that itoffers the filter designer many options, such as allowing overlappingcharacter strings, defining the length of each character string,determining the quantity of matching strings required to mark therecord(s) as classification candidates. In this way, an entity name orentity identifying information (that may include entity name, logo,phone number, address, and the like) need not be an exact match, butinstead needs enough matching character strings to exceed a kgram filterthreshold. In an example, a kgram filter may compare overlappingcharacter strings (kgram filter group) of 10 characters and may requirethat at least 10 of the character strings must match (kgram filterthreshold) for the record to be identified as a potential candidate forclassification and clustering. Because records received and processed bythe platform may have information within certain fields that may beincorrectly placed there (a personal name in an entity name field)filtering can be used to quickly separate out records that areincorrect.

Another merge technique is called classification. Classification may beperformed on any records, although records that have been identified byfiltering as candidates for classification may yield faster and morerobust classification results. Because records with non-matching entityinformation may be records of a single entity, classification uses text,language, mathematical, and other analysis techniques to identify alikelihood that two records are from the same entity. Classificationincludes a variety of techniques including canonical adaptation,specific cleanups, multi field comparison (name, address, phone number,etc), edit distance algorithms, vector generation, machine learning,decision tree, and the like.

In canonical adaptation, entity information in records is adapted toeliminate differences that should not impact classification. Differencessuch as abbreviations of words (rd. for road, ave for avenue, CA forCalifornia, and the like) can be normalized in the records. Punctuationand other characters that may have minor impact on a classification maybe removed or marked to be ignored during various classification andclustering techniques. In addition to canonical adaptation targetedcleanups may be applied to further normalize the data. Cleanups may helpto resolve deficiencies in the records such as an incorrect country oforigin, which is a common deficiency. Cleanups may be based oninformation about the domain of the records to further enhance entityidentification and merging. Cleanups may be based on geographic orregional knowledge, market verticals, industry norms, and the like. Inan example, within a market vertical, variations of textile suppliersnames may be applied to quickly align the various names to a normalizedor canonical entity name; thereby reducing the degree of complexity thatfurther classification techniques will have to deal with. The result mayinclude less complex mathematical computation. Cleanups maybe targetedat specific aspects of the records, such as entity names, city names,street names, phone numbers, and the like. Any number of these cleanupsmay be applied sequentially or in parallel to data records to improvemergability of the records.

To account for differences in data entry that may result in a very lowclassification score, classification techniques herein are applied toindividual fields (entity name field, address field) as well as tocombinations of fields (entity name+address field) so that a record withan entity name in an address field can still be identified as mergablewith other records that have the address in the address field.

Data that has been cleaned or adapted as described above and elsewhereherein may be processed through edit distance metric algorithms such asWagner-Fischer, Levenshtein, Jaro-Winkler, and the like. The result ofwhich may be a complex vector of numbers that represent dimensions ofsimilarity associated with the various classification techniquesapplied. The vectors of similarity may be based on other classificationand text analysis techniques as may be know to those skilled in the art.All such classification and analysis techniques may be applied to therecords by the platform and are included herein.

Machine learning and other artificial intelligence techniques may beapplied to determine if similarity vectors of pairs of records identifyrecords that can be merged under a common entity. Through the use oftraining vectors, and decision tree logic, record mergability may befurther assessed and a measure of such mergability may be made availableto clustering techniques. The result may include an identification ofpair-wise matches among all of the classification candidate records.

Training vectors may be derived from transaction data. A set oftransactions may be identified as a training set that may be useful inestablishing prediction parameters for associating shipments withattributes such as a type of entity, type of supplier, type of product,product feature or attribute, type of material, and the like. A trainingset may also be useful for facilitating association of a shipment withan entity by enabling development of prediction parameters that may beused therefore. By identifying candidate relationships between shipmentsand attributes or entities, training sets of transaction records mayreduce the computational load required for comprehensively filtering,classifying and clustering. In an example, a record may be presented toa processing facility such as an analysis facility J32. The processingfacility may select prediction parameters based on one or more datafields in the record. Certain fields in the transaction record may becompared to a portion of the prediction parameters to predict an entityto associate to a transaction record.

Prediction of an attribute associated with a transaction or customsrecord may be useful for rolled up, aggregated, or otherwise cumulativetransaction data. Because transaction records may be individual shipmentrecords, aggregated transaction records, rolled up or summarizedtransaction data, and the like, predicting an attribute that may beassociated with rolled up transactions may allow the platform to gainsignificant benefit from otherwise non-specific data. In an example, UScustoms records may record each shipment from China as an individualcustoms transaction record but the transactions may not identify thesupplier, just the shipper and buyer. However, China may only provide arolled up transaction that cumulates similar shipments over a period oftime, such as a calendar month. The rolled-up transaction data fromChina may have some data elements that distinguish it partially, such asa product identifier, source region, shipper, supplier, and the like.The US customs transaction data for a calendar month may be used toidentify prediction parameters that may be applied to the Chinatransaction data to predict the supplier. When US customs training setdata such as shipment quantity, shipper, and the like are applied to theChina data, a supplier may be predicted for the rolled up Chinatransaction data.

An objective of clustering is to cluster as many data records thatshould be merged under a common entity as possible. Clustering mayresult in all of the variations of one entity being identified as oneentity. A technique for clustering that may be applied is referred toherein as p-percent clustering. In p-percent clustering, a pair matchthreshold is established and any record that matches at least thethreshold percent of records in any given cluster will be added to thecluster. In this way, although pair-wise matching identifies all pairsof matched records, clustering allows records that do not all match eachother to form a cluster. In an example, if a p-percent threshold is 25%then any record that pair-wise matches at least 25% of cluster membersmay be added to the cluster. In an embodiment, dynamic p-percent mayallow dynamic adjustment of p-percent based on an aspect of the cluster,records, and the like. In an example, p-percent may be set low for asmall cluster and may be increased for a large cluster. P-percentclustering ensures that records that have strong matches to some of themembers of the cluster can be properly included in the cluster.P-percent offers significant advantages over single dimension (singlelink) clustering techniques.

Filtering, classification, and clustering are important and facilitatemerging of intra data source records (e.g. new transactions for anexisting company) as well as external data source records (e.g. US toChina customs data records). Also these techniques may be useful inclassifying entities into industries or markets.

Industry or vertical classification may be accomplished by using dataassociated with a shipping record and/or other data sources to determinewhich industry an entity (buyer, supplier) is associated with. Asdescribed above, machine learning techniques such as decision trees canbe used to classify individual data records.

The customs transaction data can be mined to automatically buildtraining data for vertical classification. Standardized codes such asthe Harmonic Tariff System (HTS) codes embedded in the free textcommodity fields can be extracted and used to determine a verticalassociated with a record. Along with the HTS code text in the commodityfield maybe mined to train a vertical classifier facility. The verticalclassifier facility can then be used to predict or determine a verticalclassification of customs records. In an example, a commodity field of arecord may be “HTS 6209180 Red cotton pants”.—The extracted HTS code6209180 may be determined to be associated with a garments industryvertical. The extracted label “red cotton pants” may be recognized asapparel in our training data. If “red cotton pants” is not recognized,then it can be added to the apparel training data. Generally only asmall fraction of customs data has HTS codes; therefore training aclassifier and applying the trained commodity entries to new records mayfacilitate classification of the remainder of the transaction record.Because the vertical classifier may be a self-learning facility, eachnew record processed by the classifier can enhance the verticalclassifier ability to classify new records. In addition, hand-labelingof records may be used to improve the vertical classifier training data.

In embodiments, a name updater may provide tools to clean the name of asupplier or buyer, such as making commas, periods, capitalization ofacronyms, fixing common misspellings, making common abbreviations, andthe like consistent. This may be an automated process of cleaning upthose names, as well as a manual interface to go through groups of namesby glancing at them.

FIG. 9 depicts a flow diagram for an overall analysis methodology forrating suppliers. Data may be collected 902, automatically 904 ormanually 908 from a variety of sources, such as customs data fromdatabases of United States customs transactions (or similar databasesfor other jurisdictions), sources of data regarding awards,certifications, and the like, databases of banking organizations, suchas the World Bank, databases of contact information (such as yellowpages, white pages and other business databases), data sources withbusiness registration information, such as containing information aboutformation of corporations, limited liability companies, partnerships andother entities, data sources with information about qualification to dobusiness in various jurisdictions, data sources relating to businesslicenses and other licensing activities, and data sources relating tovarious substantive characteristics of a business, such as Dunn andBradstreet data, data regarding corporate records, data with securitiesfilings and similar information, data from securities analysts, andvarious other sources. Such data may be brought into a data warehouse910, which may be a data mart or similar facility for handling data fromdisparate sources. Once brought into the warehouse 910, data may becleansed 912 by a variety of automatic cleaning 914 or manual cleaning918 processes, such as by automatically assigning a product category todata records associated with a supplier, based on pattern matching orsimilar techniques, such as machine learning techniques, as well asundertaking steps of anti-aliasing, assigning a caliber rating to thebuyer (such as associated with a transactional record), selecting ordeclaring a name for a buyer (such as when a record has names for morethan one party), and assigning a geographic region of shipping. Otherdata cleansing steps may be undertaken as would be understood by thosewith familiarity with data handling and manipulation. Once clean, cleandata 920 may be delivered to an analytics facility 922 for analysisaccording to various methods described throughout this disclosure,including population of modules for calculating, based on data from therecords derived from the data sources, the various ratings describedherein, including the overall ratings 110 and various component ratings.The analytics facility 922 may determine a country context, a degree ofproduct specialization, a measure of buyer loyalty, a buyer rating, orother rating. In the analytics facility ratings may be standardized,normalized or weighted, and an overall rating 110 may be calculated.Once normalized ratings 924 are generated by the analytics facility 922,ratings may be used to generate reports 928, such as an overallscorecard 200 with various constituent ratings as disclosed throughoutthis disclosure. Report generation 928 may also involve developing andpresenting percentile calculations, product categories, ratings, andcompany information.

Weights may be applied to rating algorithms, data, and the like in themethods and systems disclosed herein. Weights may be applied in theprocess of determining ratings so that certain factors that affect arating may have a greater impact on a rating than other factors. Weightsmay also be variable and may be based on a combination of factors.Weights may be applied based on a timeliness of data. Timeliness of datamay be important to be weighted because, for instance, very new data maynot yet be verified or old data may no longer represent a buyer-supplierrelationship. Weighting of data may also be important because some datamay be of suspect quality independent of age, data may not have a highdegree of relevance to a rating, and many other data quality relatedfactors. In this way, weights may be given in the rating process basedon timeliness of data, size of transaction, quality of the transactingparties, prior rating of a transacting party or entity, relevance of thedata. Weighting factors may be based on human-aided assessment of anentity, financial health of an entity, and the like.

An overall rating 110 of a supplier or buyer may be a combination ofsub-ratings such as rating associated with amount of experience,certification dimensions, county context, business metrics, customerloyalty, and the like. An overall rating 110 and any sub-rating may beweighted, normalized, and curve fitted to ensure the rating is providinga consistent reliable measure of a supplier, buyer, and the like.Additionally, the weighting may be customer specified to enable acustomer to identify portions of the ratings that are most important.

One sub-rating metric is customer loyalty. Determining a customerloyalty rating for a supplier is computationally intense andalgorithmically rich because it measures how well a supplier is atkeeping customers, or how well a buyer sticks with a supplier. In someindustries, such as in apparel, it is quite common for buyers to changealmost half of their suppliers every year. Understanding the factorsthat determine how this activity impacts customer loyalty is a keybenefit of the present invention.

Customer loyalty may be determined by looking at individualbuyer-supplier pairs. One technique to determine a customer loyaltyrating for a supplier is to determine a customer loyalty rating for eachbuyer (customer) of that supplier and then combine the individualratings. Factors that may impact customer loyalty include, a buyerbuying pattern, buying frequency, number of purchases, time since firstpurchase, and the like. Each transaction can be analyzed to determine ifthe buyer is buying from a second supplier and if the purchase is for anitem that was previously purchased from a first supplier. In thissituation, customer loyalty of the first supplier is compromised.However, simply measuring transactions may not provide a quality measureof customer loyalty. Factors such as if the first supplier has stoppedselling the item that the buyer is now buying from the second supplierare important to include.

Transaction data may often only be available as free text data (UPC andother codes may not be included in the records). Therefore the textprocessing, normalization, and canonical adaptation techniques describedherein may be beneficially applied to determining a customer loyaltyrating for a supplier. Certain aspects of buyer-supplier relationshipsmay have greater importance than others so exponential weighting on somedimensions may be useful. In an example, a longer relationship of fewertransactions may be more important than a large number of transactionsover a shorter duration. Factors included in a customer loyaltycalculation include duration of relationship, count of orders/shipments,the weight of each order/shipment (determines size/value of shipment),and the like. In an example if a buyer buys the same item from twosuppliers and consolidates orders to just one of the two suppliers, acustomer loyalty rating of the other supplier may be significantlyimpacted because of the known cutoff in the supplier-buyer relationship.

For merging records, determining an overall rating 110, and otheractivities and results associated with the platform, determiningparent-subsidiary relationships may be important. In addition toparent-subsidiary relationships, other relationships may be important indetermining overall rating 110, customer loyalty rating, and the like. Abuyer that switches from one subsidiary to another subsidiary under asingle parent may have little impact on the parent rating, but may havesignificant impact on the subsidiary rating.

An aspect of the platform that facilitates determining parent-subsidiaryrelationships may use various sources of information such as businessrecords from Dunn and Bradstreet, web news feeds, search engine resultsof business news sites, crawling of supplier web sites, press releasesof suppliers, and the like. An acquisition of a subsidiary by a parentmay be identified through one or more of these data sources and theparent-subsidiary relationship may be factored into overall rating,customer loyalty rating, merging, and the like. Parent-subsidiaryrelationships may also be determined based on predetermined heuristicssuch as same city—similar name, same buyer—similar name, and otherheuristic combinations of customs data record elements.Parent-subsidiary relationships can be determined for suppliers and forbuyers.

FIG. 10 depicts fields that are derived from customs data associatedwith supply transactions. The records depicted in FIG. 10 may comprise aportion of a buyer record of customs data 1000. Note that informationthat may be associated with a buyer's identity may reside in variousfields. FIG. 10 further illustrates fields from customs records 1002. Abuyer record of customs data 1000 may include, without limitation,fields such as shipper 1004A, consignee 1004B, notify_party 1004C,also_notify 1004D, weight 1004E, quantity 1004F, BL number 1004G,country 1004H, data 1004I, commodity 1004J, and HS code 1004K. Certainfields may facilitate identification of possible buyers based oninformation contained one or more fields; these fields may be referredto as buyer identity candidate fields 1008. In an example, one way ofidentifying a buyer may be by using the consignee 1004B. In anotherexample, notify_party 1004C may be used to identify the buyer. In yetanother example, also_notify 1004D may be used to identify the buyer.The buyer identity candidate fields 1008 may be combined in various waysto facilitate identifying a buyer.

Referring to FIG. 11 depicts a plurality of customs records with detailsthat are relevant to buyer and supplier identification and for mergingcustoms records while avoiding duplication in counting the sametransaction as a result of it being characterized in different records.Records 1102 and 1104 record the same Shipper 1004A “Shanghai BadaTextile”, Consignee 1004B “No Fear Inc.”, and HS Code 1004K “621143”.However the date 1004I is different for each record indicating thatwhile the records may be associated with the same buyer and supplierthey are not duplicate entries. Record 1108 records the same shipper1004A as records 1102 and 1104. It also records a buyer that may be thesame as the buyer of records 1102 and 1104 through the data in thealso_notify 1004D field “No Fear”. Therefore, it may be appropriate toconclude that the buyer and seller of record 1108 is the same as thosein records 1102 and 1104. However, because the HS code 1004K “621149” isnot the same, record 1108 is not a duplicate of 1102 or 1104. Record1110 records a potential buyer in consignee 1004B “No Fear” that may bethe same as the buyer in records 1102, 1104, and 1108. However, becausethe shipper 1004A “Guangzhou Textile Co” may be identified as adifferent supplier than the shipper in records 1108, 1104, and 1108, itmay be readily determined that record 1110 is not only not a duplicateof 1102, 1104, and 1108, but it also identifies a differentsupplier-buyer relationship.

Referring to FIG. 12, a customs data user interface 1204 that mayfacilitate selecting among a plurality of potential buyer names that areprovided in customs records 1202. The interface 1204 may include one ormore buyer name use buttons 1208 or some other type of selection meansfor selecting which field in each of the customs records 1202 representsa buyer name. As described in reference to FIG. 10, buyer identitycandidate fields 1008 may include consignee 1004B, the notify_party1004C, and the also_notify 1004D fields. The buyer name use buttons 1208may be associated with each field in the buyer identity candidate fields1008 so that an operator of the platform may signal which buyer identitydata item associated with each customs record 1202 should be used formerging, de-duplication, and other actions within the platform. Thecustoms data user interface 1204 is only exemplary and otherarrangements of buttons, data fields, and the like, as well as variouspresentations of the data before and after selection are possible andherein included.

A name chooser tool, such as the one described above and depicted inFIG. 12 may assist with identifying a buyer name or supplier name in arecord. The tool may allow a user to manually identify a buyer, seller,shipper, and the like for each transaction records. As described herein,there may be automated processes to deal with entity identification intransaction records. Automated or manual processes use key words like“logistics,” “trading company,” and “shipping company” to distinguishshippers from buyers or suppliers.

Referring to FIG. 13, a GUI 1300 depicting configuring mergingparameters to guide automatic merging of variations in buyer name isshown. An option button 1302 allows selection of the consignee 1004B. Inaddition, the variation to the consignee 1004B may be listed below theoption button along with check box 1304A enabling selection of one ormore variations to the consignee names 1004B. Another set of check boxes1304B listing the different variations to the consignee names 1004B maybe provided on the right side of the GUI. Selection of the option button1302, the check box 1304A and the check box 1304B may facilitate mergingof supplier names on initialization. In an example, the option buttoncorresponding to No Fear Inc. may be selected along with the check boxshowing No Fear Inc. Subsequently, the variation in the names of thebuyer can be merged.

Referring to FIG. 14, a GUI 1400 depicting configuring mergingparameters to guide automatic merging of variations in supplier name isshown. An option button 1402 allows selection of the consignee 1004B. Inaddition, the variation to the consignee 1004B may be listed below theoption button along with check boxes 1404A enabling selection of one ormore variations to the consignee names 1004B. Another set of check boxes1404B listing the different variations to the consignee names 1004B maybe provided on the right side of the GUI. Selection of the option button1402, the check box 1404A and the check box 1404B may facilitate mergingof supplier names on initialization. In an example, the option buttoncorresponding to Shanghai Bada Textile may be selected along with thecheck box showing Shanghai Bada Textile Co. Subsequently, the variationin the names of the buyer can be merged.

FIG. 15 depicts identifying factors relevant to assessing buyer loyaltyfrom transaction records. After a series of transactions 1502, 1504,1508 with one supplier, a subsequent transaction 1510 indicates that thebuyer may have switched to another supplier for a similar product. Thus,an initial loyalty period represented by transaction 1502, 1504, and1508 can be calculated, the duration of which may be the time betweenthe first order 1502 and the last order 1508 of a product from thesupplier. A switch to another supplier may terminate the loyalty period.Also the switch itself may be considered one indicator of the quality ofthe suppliers (in particular suggesting higher quality for the newsupplier and lower quality for the old supplier). In an embodiment, anegative factor may be attributed in rating the former supplier as aresult of the switch, which may balance, or even outweigh, the positivefactor associated with the previous loyalty period.

FIG. 16 depicts using transaction data that may be indicative of asupplier's degree of specialization. Customs data 1602 may include an HSCode field 1004K that may provide an indication of supplierspecialization by looking at the range of values in the HS Code field1004K for transactions records associated with a specific supplier. Alarger number of categories may suggest less specialization, while asmaller number of categories suggest more specialization.

FIG. 17 depicts steps for obtaining data indicative of a supplier'sdegree of experience. A number of units shipped, a number of orders, anda duration over which products are shipped may be factors in determiningan experience rating. Data from individual customs transaction records1702 may be aggregated and processed to determine experience factors. Inan example from FIG. 17, a duration factor of expertise may becalculated by determining the number of days between the first shipment(Jan. 2, 2005) and the last or current shipment (Mar. 8, 2005).Expertise may be in terms of how much of each product type a supplierhas shipped, such that users may better determine what suppliers havetheir greatest experience, their least experience, and the like.

FIG. 18 depicts customs data record fields 1802 that may affect asupplier's rating based on the quality of the buyers served by thesupplier. A buyer may be identified in the customs data record fields1802 through the consignee 1004B field, the also_notify 1004D field, ora combination thereof. A caliber of the identified buyer may bedetermined manually, such as by a facilitator, or by an algorithm basedon various attributes, such as size of business, number of employees,presence on a stock market, profitability, knowledge of brand amongcustomers, surveys or ratings by third parties, awards, certifications,or a range of other measures. The caliber may be stored by the platformin association with other information about the buyer. Alternatively,the caliber may be calculated from stored and retrieved information asdescribed above. The platform may calculate the caliber, a portion ofthe caliber, or may be provided with the caliber through an interface,such as a network interface.

FIG. 19 depicts a portion of a summary report 1900 showing top suppliersincluding rating 1904 of the supplier, name of the supplier 1908,supplier's location 1910 and reference details 1912. The summary may befor an industry or product category, such as women's apparel (e.g.blouses, skirts, dresses and the like). In one embodiment, a company maygive an overall rating 110 within a given category of suppliers for oneof the products, i.e., women's blouses. Further, the top suppliers ofthis category (such as the top 50 suppliers) may be listed even thoughless than 50 suppliers are shown in the embodiment of FIG. 19. Thereport 1900 may include a greater or lesser number of top suppliers.Also the report 1900 may include an executive summary portion thatprovides guidance using the summary. The ratings 1904 may be accorded tothe suppliers based on a plurality of factors such as timely supply,quality, pricing of the product and the like. Each rating 1904 may bescaled on a normalized scale, such as a one hundred point scale, withparticular ratings depicted graphically, such as in a bar graph, to makeit easier to see the relative performance of the supplier in thatcategory of rating. The ratings 1904 may also be depicted as qualitativelabels such as “Excellent”, “Good”, “Fair”, and the like. The supplierinformation, in context of the overall rating, may be provided in one ormore sections of the detailed report.

FIG. 20 depicts a report 2000 showing standout suppliers (e.g. for aparticular product), including suppliers with the highest customerloyalty and suppliers with the deepest experience in shipping to thebuyer's jurisdiction. The stand-out supplier report 2000 may include atable 2002A of top suppliers with the highest customer loyalty, and aseparate table of most experienced shippers 2002B. Each table 2002A and2002B may include a plurality of columns related to the supplier'sinformation; in an example, loyalty rating 2004A, experience rating,2004B supplier name 2008, location 2010, and details 2012 of thesupplier. In the present illustration, table 2002A may include the topfive suppliers that have the best customer loyalty. Table 2002B may listthe top suppliers with the most experience in shipping to the U.S. Inthe present illustration, a list is provided for top four suppliers thatmay have the maximum experience in shipping with their correspondingcustomers in the United States.

FIG. 21 shows an exemplary detailed report 2100 that breaks down theoverall rating 110 according to various dimensions of quality. In theexample detailed report 2100, dimensions of quality may be grouped intoperformance aspects 2102 such as track record that may include customerloyalty, amount of experience and the like; certifications 2104 that mayinclude quality management, social responsibility, and environmentalresponsibility; and business basics 2108 that may include businesslegitimacy and country context. Each rating may be scaled on anormalized scale, such as a one hundred point scale 2110, withparticular ratings depicted graphically, such as in a bar graph 2112, tomake it easier to compare the supplier performance in each dimension toeach other dimension. Ratings may alternatively be depicted asqualitative labels such as “Excellent”, “Good”, “Fair”, and the like.

FIGS. 22A and 22B show a breakdown of supplier transaction experiencefor a selected time period, which may allow prospective buyers to drawinferences as to what areas of experience are deepest for the supplier.The breakdown may include product expertise 2202 of the supplier,technical expertise 2208 of the supplier, and material expertise 2204 ofthe supplier. The product expertise 2202 may further include thepercentage distribution for a number of products; in an example, shirtsand blouses, gloves, skirts, and the like. The technical expertise 2208may include the percentage distribution of the technology applied andused by the supplier; in an example, non knitting and knitting of thematerial. The material expertise 2204 may include the percentagedistribution of the material used for the synthesis of a plurality ofproducts; in an example, silk, cotton, etc.

FIG. 23 shows a report 2300 presenting a breakdown of suppliertransaction experience according to selected factors, including a genderchart 2300A, and a customer caliber chart 2300B. These charts may bebased on a variety of supply factors including product, material,technique, shipment history, estimated minimum shipment size, averageshipment size, and the like. Report 2300 may allow the buyer to assesswhether and to what extent the supplier is likely to have expertiseapplicable to the buyer's position in the marketplace.

FIGS. 24 and 25A-B show a breakdown of supplier shipment history, whereshipment history may be broken down by piece count, by month, by monthto a certain country, and the like. FIG. 24 depicts a breakdown ofshipment history as a piece-count chart 2400. FIGS. 25A-B break downshipment history into a monthly article chart 2502A and a monthlyshipment count chart 2502B. In embodiments, the product may includeshipment history graphs which show trends and volumes of shipments(quantified in terms of shipping containers) made over some period oftime. Embodiments may also show the number of articles, garments,pieces, or, generally, entities, of the shipped product shipped overtime based on algorithms that take into account the weight of thecontainer and the assumed weight of each individual entities inside thecontainer. Embodiments may also include a characterization of how largea supplier's shipments tend to be in terms of number of entities pershipping container. Such a characterization may allow a furthercharacterization of whether a supplier may be able to fulfill smallorders, if they will be willing to fulfill large orders, and the like.Embodiments may also include estimates of a supplier's monthly capacityand their smallest shipment size.

FIG. 26 shows a user interface through which users may search forsuppliers. The supplier search interface 2602 may allow a user to searchfor suppliers based on category 2608A, name 2608B, and country 2608C. InFIG. 26, a user has selected to search for a supplier based on thesupplier's country 2608C. A user may enter text in the text entry box2610 that may be useful in determining a country and then the user mayselect search control 2604 to search for suppliers within a country thatmay be determined from the text input into box 2610.

Referring to FIG. 27, the search may also be conducted to obtaininformation regarding the various entities 2708 such as suppliers 2732and buyers 2730. The computer implemented facility 2702 may collect andstore a plurality of public records of transactions 2704 among aplurality of buyers 2730 and suppliers 2732. The transactions 2704 maybe aggregated and associated with the entities 2708 (suppliers and/orbuyers). A user interface 2722 may be provided that may facilitate auser who may be searching for at least one of the entities 2708 and theinformation associated with the at least one of the entities 2708 fromthe aggregated transactions data. The user may be any person interestedin retrieving the above information; the user may also be a supplier, abuyer, a third party, and the like. The examples of user interface 2722may include a Graphical User Interface, Web-based User Interface, TouchInterface, and some other types of user interfaces.

In an embodiment, the user interface 2722 may facilitate a tuple-basedsearch 2748. The tuple-based search 2748 relates to a capability ofsearching for entities 2708 related to a specific parameter. Suchparameter may relate to a product 2750, a material 2752, and/or atechnique 2754. In an example, a supplier S1 may like to conduct asearch for buyers available in the United States for ‘Aluminum basedpackaging sheets formed by extrusion.’

In accordance with an embodiment of the present invention, the searchresults obtained from the above described searches for the entities 2708may also be ranked. In an embodiment, the ranking may be based on asupplier rating.

In an embodiment, the rating may be based on the context of a party, thebusiness legitimacy of a party, an assessment based on the tradingenvironment of a country, macroeconomic information, industry awards,industry certifications, amount of experience, number of shipments,duration of experience, size of transactions, extent of domesticexperience, extent of international experience, caliber of customers,customer loyalty, degree of specialization, specialization in productcategories, specialization in manufacturing techniques, specializationin materials, specialization in gender, feedback from customers,feedback from buyers, feedback on product quality, feedback on customerservice, feedback on timeliness of delivery, feedback on languageskills, feedback on sample making ability, respect for intellectualproperty, quality management, social responsibility, environmentalresponsibility, standards of compliance, certifications, certificationswith respect to specific vendor standards, risk profile 2758,opportunity profile 2760, and some other types of factors andparameters.

Referring to the above example again, upon searching, a supplier 2732may obtain a list of buyers 2730, which may be interested in buying‘Aluminum based packaging sheets formed by extrusion’. In addition, thesupplier may like to ascertain the best buyers. For this purpose, thesupplier 2732 may also obtain a ranking of the buyers 2730 based onselected parameters such as feedback reports, risk associated with eachbuyer, geographical location, and some other type of parameter. Therating may be in the form of a value, integer, percentage, and someother forms of ratings. Based on this rating, a ranking may be providedto each of the buyers 2730. This ranking may in turn facilitate thesupplier 2732 in making the judgment regarding the appropriate buyer.Risk may be related to counterfeiting, capacity, subcontracting,political factor, geographic factor, weather factor, geology factor,financial risk, probability of non-performance of a contract,probability of termination of a contract, intellectual property,targeted delivery date, transactional customs data for a party otherthan the supplier and/or buyer, likelihood that a buyer will move to analternative supplier, non-payment and some other types of risk factors.

An opportunity profile 2760 may be an assessment of the potential fornew business opportunities determined from customs transaction data. Byanalyzing transactions in customs data, buyers and suppliers canidentify potential business opportunities such as to establish a newrelationship, reduce costs, increase availability, and the like. Whilecompanies guard much of their internal information related to costs andprofit, the transaction information available in public customs recordscan provide great insight into ongoing buy and sell activity. In anexample of opportunity profile 2760 assessment, a buyer may decide thereis an opportunity to push a supplier harder to reduce a price. The buyermay be able to determine that the supplier has made fewer sales (e.g. asevidenced by lower shipment quantities in customs transaction records)over time. One potential reason for this is that a competitor of thesupplier is offering a lower price. Therefore the supplier may need toreduce price to remain competitive. likewise the supplier can review thesame records and determine that the competitor is selling at a lowerprice under certain conditions, so the supplier can device a counterpricing strategy accordingly. In another example, a supplier may spot anopportunity to sell additional types of products to an existing buyer byexamining the transactions of the buyer. The supplier may determine thatthe buyer is purchasing a type of product from a competitor that thesupplier also offers but is not currently selling to the buyer. Thesupplier could provide the buyer with the opportunity to potentiallyimprove the buyer costs by ordering the product from the supplier ratherthan the competitor. Factors such as combined volume pricing, reducedaccounting overhead, lower shipping costs and the like may be keybenefits that the supplier can use to entice the buyer.

Likewise the platform or an operator of the platform may use the customstransactional data to identify and suggest opportunities to buyersand/or suppliers. The transactional data may be analyzed for factorsthat indicate the potential for an opportunity and the opportunity maybe prepared as an offer to one or more of buyers, suppliers, and thelike. Opportunities may include availability of pricing leverage for abuyer with respect to a supplier; consolidation of orders with asupplier, pricing leverage for a supplier with respect to a buyer,increasing a share of a buyer's total spending for a supplier, and thelike.

A risk profile 2758 may be determined based on analysis of customstransaction data. A risk profile for a supplier or a buyer may be basedon customs transaction data for the supplier, the buyer, or a thirdparty. A risk profile that may be determined from customs transactionaldata may include risk related to counterfeiting, capacity overload,subcontracting, political factors, geographic factors, weather, geology,finances, probability of non-performance to a contract, probability oftermination of a contract, intellectual property, achieving a targeteddelivery date, non-payment, selecting an alternate supplier, ordercancellation, order push-out, and the like. A risk profile that may bederived from customs transaction data may be a basis for determiningterms and conditions of insurance, and the like.

The above description disclosed that the search interface may beutilized for searching entities 2708 based on the aggregatedtransactional data. In an embodiment, the suppliers 2732 may also besearched based on the region of interest (geography) 2738, industryspecialization 2740, customers (entity types) 2742, and the interestdisplayed in forming relationship (likelihood of interest) 2744. Thismay be explained in detail in conjunction with FIG. 27.

Referring to FIG. 27 again, the computer implemented facility 2702 maycollect and store the public transaction records 2704 and associatethese transactions with the entities 2708. A search facility 2720 maysearch for an entity based on a particular search attribute 2734. Thesearch attribute 2734 may be a type of entity 2742, geographic region2738, industry specialization 2740, and likelihood of interest in atransaction with the user or the search 2744.

In embodiments, the search facility 2720 may be adapted to be used by abuyer for searching a supplier. Alternately, the search facility mayalso be adapted to be used by a supplier for searching a buyer.

In an example, a buyer 2730 may like to search for suppliers (in US) of‘automotive machine parts’ that may be willing to do business with asmall offshore firm outside United States. Therefore, in the abovescenario the likelihood of interest that the suppliers may display maybe based on the location and size of the firm.

In embodiments, methods and systems disclosed herein may include aninterface by which buyers may search for suppliers, as disclosed above.The search interface may allow buyers to query a database of supplierinformation organized in a hierarchy according to product categories, inorder to find suppliers who provide products in a selected category. Abuyer may then select particular suppliers and obtain an online profileor report, as described throughout this disclosure, as to attributes ofa particular supplier. In embodiments, the search interface allows thebuyer to search by product category, material used to make the product,or technique used to make the product, among other attributes. Filteringtools may be provided in the interface to allow the buyer to sort databy product type, material, technique, caliber of customer, or otherattributes, to expand or group data, to drill down into particularcategories or sub-categories, and the like.

In embodiments, the database of supplier information includes anontology of product categories, which may include a tree of categoriesand sub-categories of all types of products found in various datasources, such as customs records databases.

In embodiments, filters may be enabled, allowing a buyer to search alongdimensions of the data. In an example, if a buyer wishes to search forsuppliers who work with a particular material, a filtering algorithm maytake the union of all materials used by suppliers and present thosematerials as filters by which a set of suppliers may be selected bybuyers for further analysis. The filters may be presented in a graph ortree structure, so that a user may check a box to expand or contract aparticular portion of the tree, thereby allowing filtering bysub-category down to the leaf node in a tree. In embodiments, data arerepresented in tuples and results for a particular filter are ordered,such as by overall rating of the supplier. Results for a particularfilter may also be ordered by other features, such as most specializedand the like.

Filters may include construction techniques, dyeing, washing andembellishing techniques, gender of the product, company type, country ofsupplier, and the like. When data are represented in tuples, allproducts a supplier has made may be represented by material,sub-material, and technique (e.g., cotton—poplin—knitted sweater). In anexample, when a search is conducted for a cotton poplin sweater, thesuppliers who have made cotton poplin sweaters can be retrieved (not theunion of ones who have made cotton or poplin sweaters in this example).The tuple concept applies to children of each concept in a hierarchy, soif the user selects cotton, the user will receive results for cotton andall children of cotton in the materials hierarchy.

In embodiments, the search interface may include a non-tuple-basedsearch mode in which suppliers would be suggested as possible matchesfor the user's query that would be the union of the search terms. In anexample, if a supplier has worked with silk, and has produced pants, thesystem predicts that this supplier could make silk pants.

In embodiments, a user interface may include paid or sponsored links inaddition to search results derived from the rating platform describedherein.

In embodiments, methods and systems disclosed herein may include privateand public versions of reports, where a searcher can get to a publicprofile by an Internet site but requires some additional relationship(possibly involving payment) in order to drill down to receive moreinformation, such as a complete profile of a supplier.

In embodiments, various icons, filters, sliders or other techniques maybe provided in a user interface to allow a user to explore informationabout a supplier. In embodiments, a user can click for “details,”thereby pulling up a ranking a supplier has for a given dimension, withinformation about the data source and a reminder of the purpose of thatdimension. In embodiments, icons may show if a score is high, medium orlow, thereby bucketing suppliers into general categories. Inembodiments, filters or sliders may allow users to refine results, suchas to show suppliers only if the product in question represents at leasta minimum percentage of that supplier's product mix.

In embodiments, an interface may be provided for rating a supplier, suchas on dimensions including an overall rating, product quality, customerservice, timeliness, English language capability, sample-making ability,respect for intellectual property, and the like. Buyer ratings may beaveraged or otherwise normalized and reported as part of a supplier'soverall rating 110. In embodiments, transactional data may be used toensure that a transaction occurred (to keep ratings unpolluted). If abuyer rating is good, this can give a significant boost to an overallrating 110.

In embodiments, buyers could specify which dimensions are most importantto them, and the overall rating 110 could be customized and weightedaccording to the buyer's preferences.

In embodiments, suppliers may be suggested to buyers based on the typesof qualities the buyer seems to appreciate, and the types of productsthe buyer has produced in the past.

The capability to identify and classify various buyers and suppliers as‘friends’ or the like may also be facilitated by using publictransaction records 2804, as shown in FIG. 28. The examples of publicrecords may be government registration records, evidences of businesslegitimacy, custom records, data sheets and reports for work order,audit records, bank records and some other types of public recordsdepicting various transactions. This may in turn help both the buyersand the suppliers to identify similar buyers and suppliers, and in turnhelp them make decisions regarding collaborations, competition, and someother types of strategic positioning.

Referring to FIG. 28, a computer implemented facility 2802 may be usedto collect and store public records of transactions 2804. The publicrecords may be government registration records, evidences of businesslegitimacy, custom records, data sheets and reports for work order,audit records, bank records and some other types of public recordsdepicting various transactions. The transaction records 2804 may beassociated with various entities (such as corporations, items, buyers,suppliers, third parties, etc.) and may generate information that may beaggregated transaction information (transactions associated with saidentities). An analysis may be performed for classification 2828 ofentities 2808. The classification 2828 may be a likeness basedclassification 2830. The likeness based classification 2830 may indicatethat the suppliers, buyers and the third parties may be classifiedaccording to the type, or degree of likeness, types of qualitiesappreciated, past experience or some other characterization parameter.It may be noted that the classification may be conducted to classify atleast one of a supplier and a buyer according to any one of thecharacterization parameters.

In embodiments, a buyer may identify like (similar) buyers. Similarly, asupplier may identify similar suppliers.

In other embodiments, a supplier may identify buyers like those of thesupplier. A buyer may also identify suppliers like those of the buyer.

In embodiments, a buyer may identify suppliers of a specified type.Further a supplier may identify buyers of a specified type.

In embodiments, a buyer may identify suppliers most likely to prefer aparticular buyer. Similarly, a supplier may identify buyers that wouldprefer a specific supplier.

In embodiments, the public records of transactions 2804 may also be usedfor classification of buyers 2838. This has been explained inconjunction with FIG. 28. The public records of transactions 2804 storedin the computer implemented facility 2802 may store transaction records2804 relating to various suppliers 2840 and buyers 2838. The informationassociated with the public records of transactions 2804 in relation tovarious entities 2808 may be further analyzed. Based on the analysis,buyer classification 2832 may be performed to classify various entitiesidentified in the transactions into buyers' category. It may beappreciated that the above described process and the system may also beused for classification of various entities into suppliers' category(supplier classification 2834).

In embodiments, an interface may include a capability for buyers tonetwork, chat or otherwise interact with each other with respect tosuppliers. Such a network may include a capability of identifying otherbuyers as “friends” or the like, thereby allowing sharing of informationonly among trusted parties. In such a case, information about suppliersfor particular buyers might be automatically populated, simplifying thesharing of information about experiences with particular suppliers usedby a network of buyers.

In embodiments, analyses could be used to assess and/or identify creditworthiness of suppliers or buyers.

In embodiments, ratings could be embedded into other media such as otherwebsites, emails, print media, and the like. Such embeddings could be aresult of calls to an Application Programming Interface (API), or othermethods.

In embodiments, ratings may be grouped into buckets, such as“excellent,” “good,” “fair,” “poor,” and “not trade worthy.” Variousmethods may be used to group suppliers into such buckets. In an example,“excellent” ratings may be given to suppliers who have businesslegitimacy and are in the top quartile in both loyalty and experience,“good” ratings may be given to suppliers who have business legitimacyand are in the top half in both loyalty and experience, “fair” ratingsmay be given to suppliers who have business legitimacy and are in thetop half in either loyalty or experience, “poor” ratings may be given toother suppliers who have business legitimacy, and “not trade worthy”ratings may be given to suppliers who do not have indicia of businesslegitimacy.

In embodiments, methods and systems disclosed herein may assistsuppliers in generating leads among buyers for opportunities to supplyproducts. Information about how to improve ratings may be used to assistsuppliers in generating high quality leads.

The methods and systems of the platform may facilitate identifying asupplier of an item type that is disclosed in a transaction record evenif the supplier is not a party to the transaction. Identifying asupplier of an item, or a type of item may benefit buyers, suppliers,and the like by identifying potential new relationships between buyersand suppliers. A buyer may use the resulting supplier identification andother information in transaction records, such as declared customsvalue, to compare a current supplier cost with a different supplier costfor the same item. A buyer or a potential buyer may use at leasttransaction cost and delivery information to identify suppliers fromwhich the buyer may request a quote for supplying the item. Suppliersmay identify buyers of products that the supplier also provides. Thismay lead to efficient marketing and sales activity for the supplierbecause the supplier would know that buyer has a significant interest inthe item being purchased. By examining other information in thetransaction, such as buyer behavior, transaction history and the like,the supplier may identify an offer profile of the buyer and present avery well targeted offer to the buyer.

FIG. 29 depicts a process for gaining these advantages from the methodsand systems herein. The process depicted supports identifying a supplierof an item in a first transaction record by comparing the item in thefirst record to a second record. When a match is found, the supplieridentified in the second record may be determined to supply the item.After other conditions are met, such as country preference, supplierrestrictions, and the like the supplier can be reported. The process canbe repeated for any number of second transactions. The process could beperformed in a similar way for determining a buyer of an item. Inparticular, a plurality of transaction records 2902 may be collected andpresented to the process 2914. A reference record 2930 that includes areference product identifier 2908 or even just a product identifier 2908can also be an input to the process. After retrieving one of thepluralities of transaction records 2902 through the retrieval step 2914,the product identifier 2904 of the retrieved transaction record iscompared in step 2918 to the reference identifier 2908. If there is asufficient match between the two product identifiers 2904 and 2908, thesupplier identity 2912 is captured from the retrieved transaction recordin step 2920. If the supplier in the retrieved transaction record 2912is determined in step 2922 to be different than the reference supplier2910, additional conditions, such as the supplier location and the likemay be evaluated in step 2924 by looking at the retrieved transactionrecord and other data 2932 associated with supplier 2912 that may beavailable to the platform. If the other conditions are not met in step2924, additional transaction records may be retrieved in step 2914. Theprocess may be repeated any number of times based on various parametersthat can be used to control the process, such as a number of potentialsuppliers to identify, a number of records to retrieve, a number oftransaction records that are available, and the like. In the embodimentof FIG. 29, elements 2912 and 2910 could represent a buyer instead of asupplier. Also in step 2922, a desirable outcome may be a match between2912 and 2910. These and other variations in the process of FIG. 29 thatfacilitate matching buyers or suppliers with an item or product type areincluded herein.

In accordance with an embodiment of the present invention, theinformation from the aggregated transactions may also be utilized forsupplier assessment. Supplier assessment may involve determining if aspecific buyer has ceased business operations with a supplier. Such adetermination may be based on a cycle time between shipments which maybe based on historical shipment data derived from transaction records. Acalculation of cycle time of shipments for an item from a supplier to abuyer may indicate an approximate date of a next shipment. If atransaction record reflecting the next shipment does not show up in thetransaction records within some period of time beyond the indicated nextshipment date, the methods and systems may indicate that the buyer mayhave stopped business operations with the supplier. The nature of thestoppage may be further determined if transaction records indicate thatthe buyer has begun receiving shipments of the item from a differentsupplier. Cycle time calculations may also be used to evaluate asupplier's delivery performance. Significant increases in cycle time mayindicate delay of shipment by the supplier. An assessment ofsupplier-buyer transaction status may also include factoring in buyerinventory. Buyer inventory may be factored in as a prediction orestimate of inventory.

In an embodiment of the present invention, methods and systems may beprovided for rating an entity based on rolled up customs data. Rolled upcustoms data may include aggregated, cumulative, summary, or similarmethods of combining a series of transactions into roll-up data.Rolled-up data may include a total of shipments over a period of timefor a buyer-seller-product association. Rolled-up data may include totalshipments over a period of time for product-shipper association. Any andall types of consolidation of transaction data that may be based on atime interval, a frequency, a region, an industry, a product, asupplier, a buyer, a shipper, a source region, an exchange rate, and thelike are herein included. In an example, rolled-up transaction data mayinclude a supplier's total output in each product category over acalendar month. In another example a country may report a total exportof a product during a week. In both cases critical transactioninformation that may be missing may be estimated or predicted in orderto develop otherwise useful information from the rolled-up information.The computer implemented facility 2902 may collect and store therolled-up public records of transactions 2904 and aggregate them to formaggregated transactions.

In an embodiment, the transaction records may relate to the shipmenttransactions.

Further, the aggregated transaction records may be associated with aparticular supplier. This associated information may be analyzed todetermine and convey the rating for the supplier.

It may be appreciated that this procedure may be conducted periodically(In an example, every three months). In another embodiment, the changein rating of a specific supplier may be presented as an alert.

In an example, the embodiments described above may be utilized by acompany dealing in an improved form of pesticide that may wish todetermine the ratings of a specific supplier of raw materials situatedin a different country. Therefore, the aggregated and associatedshipment transaction information (regarding the shipment time, schedule,price and delivery) for the supplier may be used to determine its ratingamong a plurality of similar suppliers. Subsequently, this rating may beinstrumental in helping the above company make supply related businessdecisions.

The public records of transactions may also be utilized for predictingminimum order requirements for a factory. Referring to FIG. 30, thecomputer implemented facility 3002 may collect a plurality of publictransaction records 3004. The collection step may be performed by acollection facility 3010. The collected records may be stored by astorage facility 3012. Upon collection and storage the plurality ofpublic transaction records 3004 may be aggregated by the aggregationfacility 3014 and associated with various entities 3008.

In an embodiment, the entity may be a factory.

In another embodiment, the entity may be a supplier.

In yet another embodiment, the entity may also be a subsidiary of asupplier.

In an example, information regarding the public transaction records 3004such as transaction receipts for a candle supplier selling a batch offactory-made candlesticks from a candle manufacturer may be aggregatedand associated by the computer implemented facility 3002. The analysisfacility 3020 may perform detailed analysis of this information togenerate various types of results. In an embodiment, the analysisfacility 3020 may predict the minimum order requirement for an entity,based on the analysis of the transactions. As described in the aboveexample, the analysis facility 3020 may predict the number of batchesthat the candle manufacturer may need to sell in order to cross theminimum profit mark. In another example, the analysis facility 3020 maypredict the minimum number of candle-stick batches that may need to besupplied to a third party in order to fulfill the required terms laiddown in a mutual contract. In yet another scenario, the analysisfacility 3020 may also facilitate predicting the minimum orderrequirements that a subsidiary of a supplier may need to supply amongthe batch of suppliers.

In embodiments, methods and systems are disclosed herein for usingdisparate data sources, including transactional records, such as fromcustoms transactions, as a basis for rating suppliers of products. Inembodiments, transactional data from actual transactions are used togenerate experience ratings, specialization ratings, customer loyaltyratings, or other ratings.

In embodiments, a rating system is provided in which buyers ratesuppliers of products, wherein transactional data, such as from customsrecords, are used to verify the legitimacy of the feedback, such as toverify that a rated transaction actually occurred.

In embodiments, methods and systems allow buyers to search forsuppliers, including with filters based on product category, material ortechniques offered or used by the suppliers, and to retrieve ratingsinformation about the suppliers, including ratings derived fromtransactional data (such as customs data) or ratings derived from otherbuyers.

In embodiments, a platform for enabling searches for suppliers andratings of suppliers may include various tools, such as tools formerging records, merging supplier names, and the like.

In embodiments, methods and systems disclosed herein may include a quotetool by which buyers may identify suppliers and then generate a requestfor a quote from selected suppliers.

In embodiments, algorithms may be used for determining a pricingleverage metric, such as based on transactional data, such as customsrecords. In an example, a supplier's pricing leverage may depend on thepercentage of a supplier's shipments that are going to a single buyer,the proximity to a recent switch in supplier by one or more buyers, asupplier's overall score, a supplier's customer loyalty score, asupplier's experience in an area, and global factors, such as overalldemand for a product offered by a supplier. Thus, an interface may allowbuyers to assess pricing leverage based on calculations using one ormore of these factors, normalized or weighted to provide an overallestimate or score as to pricing leverage of a supplier.

In embodiments, access to the database may be restricted by the capacityof a supplier, and the ability of a supplier to ship small quantities.For instance, large buyers may need access to the entire database, whilesmaller buyers may need special access to suppliers that specialize insmaller orders.

A user interface may include various alerts, such as an alert for when anew supplier satisfies a search criterion of a buyer.

Methods and systems disclosed herein may include methods for syndicatingdata, such as delivering overall scores, category ratings (e.g.,“excellent,” “good,” “fair,” or “poor”) or the like, to third parties,such as for presentation in connection with other business data, such asdata presented to securities analysts, data presented to buyers forother purposes, and the like. Users will be able to provide subjectiveratings of suppliers on such third party presentations using an API.

In embodiments, methods and systems disclosed herein may includecollaborative filtering techniques, such as to allow buyers to seeinformation relevant to other buyers who share characteristics with thebuyer (such as conducting similar searches, using similar suppliers, orhaving similar transaction records). Collaborative filtering may alsoallow suppliers to access information relevant to other suppliers withsimilar characteristics, such as ratings for suppliers to supply thesame types of products, to the same types of buyers, and the like.

In embodiments, a buyer scorecard 200 may be provided that shows summarydata for a supply chain of various suppliers, such as to indicate howthe buyer's suppliers collectively compare to suppliers of other buyers,such as competitors of the buyer.

In embodiments, a supplier comparison tool may be used to comparesuppliers on various attributes.

In embodiments, buyers may be rated on behalf of suppliers, such asbased on loyalty to suppliers.

In embodiments, ratings of suppliers or buyers as described throughoutthis disclosure may be used for third parties, such as, in embodiments,financial analysts. In an example, an analyst could evaluate the qualityof a company's supply chain based on collective supplier ratings.Similarly, an insurance company could use data about suppliers of abuyer to assess supply chain risk, such as for analyzing risk associatedwith insurance associated with activities of suppliers.

In embodiments, buyers may supply data to the platform described hereinin order to assist with developing ratings, but that data may bemaintained as proprietary to the buyer, such as to keep ratingsgenerated based on that data private to the buyer.

In embodiments, information about suppliers may be syndicated to desksoftware tools, such as tools used by purchasing managers and buyingstaff within buyer organizations. Thus, reports or ratings may be fed sothat they appear within the interface of one or more other desktop orweb-based tools used by such users.

In embodiments, methods and systems disclosed herein may includefiltering tools for sorting data retrieved from customs recordsaccording to an industry hierarchy, such as a hierarchy of products,materials and techniques.

In embodiments, a search interface may allow for a search based onsupplier capability, such as based on information retrieved fromtransactional data, such as customs records.

In embodiments, a data analytics platform may be provided for analyzingsupplier capabilities, such as based at least in part on transactionaldata about supplier activities, such as transactional data from customsrecords.

In embodiments, a rating system may be based on a combination of customsdata and other data, such as data based on an internal database oftransactions made by an agent on behalf of buyers transacting withsuppliers.

In embodiments, a platform may include a transactional facility, such asfor allowing buyers to transact with suppliers that have been identifiedby the search and ratings facilities described herein. Suchtransactional facility may include modules related to ordering, pricing,payment, fulfillment, and the like.

Referring to FIG. 31, in accordance with the methods and systemsdescribed herein, the public records of transactions 3104 may beutilized for rating a sub-entity of a supplier 3108. The computerimplemented facility 3102 may collect and store the public transactionrecords 3104 among the plurality of buyers 3130 and suppliers 3132. Uponaggregating and associating the transactions 3104 with the entities 3108(such as buyers and suppliers), an analysis may be performed regardingthe sub-entities of the suppliers 3132. Examples of sub-entities 3140may include a factory, a group of factories 3142, subsidiaries 3144, andsome other types of entities.

In an example, the aggregated transactions information may reveal a.list of twenty entities doing business in an uptown market. A searchermay utilize the methods and systems disclosed herein to determine a listof seven entities that may be sub-entities for a specific supplier S1.In accordance with the embodiments of the present invention, these sevenentities may be rated based on the transactional data. The sevenentities (say 2 factories, 3 subsidiaries, and 2 sales divisions) may berated based on the timeliness of the delivery, feedback from the buyers,and so on.

In an embodiment, the analysis facility 3124 may determine thesub-entities 3140 for a supplier from the group of entities. Thedetermination of sub-entities may be based on the analysis of the publictransaction records 3104. In an embodiment, the public transactionrecords 3104 may be customs transaction records.

In another embodiment, the sub-entities 3140 of the supplier may berated based on the analysis of the aggregated transactions and otherinformation and parameters as explained throughout the disclosure.

The elements depicted in flow charts and block diagrams throughout thefigures imply logical boundaries between the elements. However,according to software or hardware engineering practices, the depictedelements and the functions thereof may be implemented as parts of amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations are within thescope of the present disclosure. Thus, while the foregoing drawings anddescription set forth functional aspects of the disclosed systems, noparticular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context.

Similarly, it will be appreciated that the various steps identified anddescribed above may be varied, and that the order of steps may beadapted to particular applications of the techniques disclosed herein.All such variations and modifications are intended to fall within thescope of this disclosure. As such, the depiction and/or description ofan order for various steps should not be understood to require aparticular order of execution for those steps, unless required by aparticular application, or explicitly stated or otherwise clear from thecontext.

The methods or processes described above, and steps thereof, may berealized in hardware, software, or any combination of these suitable fora particular application. The hardware may include a general-purposecomputer and/or dedicated computing device. The processes may berealized in one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as computer executable codecreated using a structured programming language such as C, an objectoriented programming language such as C++, or any other high-level orlow-level programming language (including assembly languages, hardwaredescription languages, and database programming languages andtechnologies) that may be stored, compiled or interpreted to run on oneof the above devices, as well as heterogeneous combinations ofprocessors, processor architectures, or combinations of differenthardware and software.

Thus, in one aspect, each method described above and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, means for performing thesteps associated with the processes described above may include any ofthe hardware and/or software described above. All such permutations andcombinations are intended to fall within the scope of the presentdisclosure.

While the invention has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present invention isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law. All documents referenced herein arehereby incorporated by reference.

What is claimed is:
 1. (canceled)
 2. A method, comprising: using acomputer implemented facility to collect and store a plurality of publicrecords of transactions, the public records of transactionscorresponding to a plurality of transactions between a plurality ofbuyers and a plurality of suppliers; determining an inferred entityidentifier for each of the plurality of public records of transactions;identifying groups of the inferred entity identifiers, wherein eachgroup of the inferred entity identifiers connote a corresponding commonentity, wherein the identifying is in response to a similarity of datain the public records of transactions associated with each of theinferred entity identifiers; processing a plurality of the groups ofinferred entity identifiers to determine public records of transactionsof each of the groups of inferred entity identifiers that are inferiorcandidates to be merged under the corresponding common entity, andremoving the inferior candidates from each of the groups of inferredentity identifiers, thereby obtaining a plurality of filtered groups ofinferred entity identifiers; merging the public records of transactionsof each of the filtered groups of inferred entity identifiers into acorresponding filtered common entity record set; aggregating the publicrecords of transactions for at least one of the filtered common entityrecord sets; and associating an entity type with the common entity inresponse to the aggregating.
 3. The method of claim 2, wherein detectinggroups of inferred entity identifiers comprises configuring eachinferred entity identifier into a confidence band of a plurality ofconfidence bands, each confidence band suggesting one of automatedmerging, human-aided merging, or no merging.
 4. The method of claim 2,further comprising classifying at least one of the inferred entityidentifiers into entity classes through analysis of content of thepublic record of transactions associated with at least one of theinferred entity identifiers.
 5. The method of claim 2, furthercomprising executing a cleanup algorithm that facilitates correction ofcountry of origin data in a plurality of the plurality of public recordsof transactions in response to analysis of free text fields in theplurality of the plurality of public records of transactions.
 6. Themethod of claim 2, wherein processing the plurality of the groups ofinferred entity identifiers to determine public records of transactionsof each of the groups of inferred entity identifiers that are inferiormerging candidates is performed by applying a computer searchingalgorithm to facilitate determining the inferior candidates to beremoved.
 7. The method of claim 6, wherein applying the computersearching algorithm comprises searching free text fields of the publicrecords of transactions for at least one of a word, term, and phrasethat corresponds to an ontology of key text values.
 8. The method ofclaim 2, further comprising providing a user interface whereby a usermay search for an entity and retrieve relevant information based atleast in part on the aggregated public records of transactions.
 9. Themethod of claim 2, further comprising applying a monitoring algorithmthat facilitates detecting statistical measures that indicate variancesin the public records of transactions.
 10. A method, comprising: using acomputer implemented facility to collect and store a plurality of publicrecords of transactions, the public records of transactionscorresponding to a plurality of transactions between a plurality ofbuyers and a plurality of suppliers; determining an inferred entityidentifier for each of the plurality of public records of transactions;identifying similarities of data in the public records of transactionsassociated with each of the inferred entity identifiers; generatinggroups of inferred entity identifiers based on the identifiedsimilarities; processing the public records of transactions within eachgroup of inferred entity identifiers to identify inferior candidatesfrom each group of inferred entity identifiers, and removing theinferior candidates from each group of inferred entity identifiers,thereby obtaining filtered groups of inferred entity identifiers; foreach filtered group of inferred entity identifiers, merging the publicrecords of transactions of the group into a corresponding filteredcommon entity record set; and aggregating the public records oftransactions for at least one corresponding filtered common entityrecord set.
 11. The method of claim 10, wherein the generating groups ofinferred entity identifiers comprises configuring each inferred entityidentifier into a confidence band of a plurality of confidence bands,each confidence band suggesting one of automated merging, human-aidedmerging, or no merging.
 12. The method of claim 10, further comprisingclassifying at least one of the inferred entity identifiers into entityclasses through analysis of content of the public record of transactionsassociated with at least one of the inferred entity identifiers.
 13. Themethod of claim 10, further comprising executing a cleanup algorithmthat facilitates correction of country of origin data in a plurality ofthe plurality of public records of transactions in response to analysisof free text fields in the plurality of the plurality of public recordsof transactions.
 14. The method of claim 10, wherein processing theplurality of the groups of inferred entity identifiers to determinepublic records of transactions of each of the groups of inferred entityidentifiers that are inferior merging candidates is performed byapplying a computer searching algorithm to facilitate determining theinferior candidates to be removed.
 15. The method of claim 14, whereinapplying the computer searching algorithm comprises searching free textfields of the public records of transactions for at least one of a word,term, and phrase that corresponds to an ontology of key text values. 16.A method, comprising: using a computer implemented facility to collectand store a plurality of public records of transactions, the publicrecords of transactions corresponding to a plurality of transactionsamong a plurality of buyers and a plurality of suppliers; using thecomputer implemented facility to collect and store a plurality ofrecords other than public records of transactions associated with atleast one of: at least a portion of the buyers or at least a portion ofthe suppliers; determining an inferred entity identifier for each of theplurality of public records of transactions and each of the plurality ofrecords other than public records of transactions; identifying groups ofthe inferred entity identifiers, wherein each group of the inferredentity identifiers connote a corresponding common entity, wherein theidentifying is in response to a similarity of data in the public recordsof transactions and the records other than public records oftransactions associated with each of the inferred entity identifiers;processing a plurality of the groups of inferred entity identifiers todetermine inferior candidates of at least one of the public records orthe records other than public records of transactions of each of thegroups of inferred entity identifiers; removing the inferior candidatesfrom each of the groups of inferred entity identifiers, therebyobtaining a plurality of filtered groups of inferred entity identifiers;merging the records of each of the filtered groups of inferred entityidentifiers into a corresponding filtered common entity record set; andaggregating at least one of the public records of transactions or therecords other than public records of transactions for at least one ofthe filtered common entity record sets.
 17. The method of claim 16,wherein identifying groups of inferred entity identifiers comprisesconfiguring each inferred entity identifier into a confidence band of aplurality of confidence bands, each confidence band suggesting one ofautomated merging, human-aided merging, or no merging.
 18. The method ofclaim 16, further comprising classifying at least one of the inferredentity identifiers into entity classes through analysis of content ofthe public record of transactions associated with at least one of theinferred entity identifiers.
 19. The method of claim 16, furthercomprising executing a cleanup algorithm that facilitates correction ofcountry of origin data in a plurality of the plurality of public recordsof transactions in response to analysis of free text fields in theplurality of the plurality of public records of transactions.
 20. Themethod of claim 16, wherein processing the plurality of the groups ofinferred entity identifiers to determine public records of transactionsof each of the groups of inferred entity identifiers that are inferiormerging candidates is performed by applying a computer searchingalgorithm to facilitate determining the inferior candidates to beremoved, wherein applying the computer searching algorithm comprisessearching free text fields of the public records of transactions for atleast one of a word, term, and phrase that corresponds to an ontology ofkey text values.
 21. The method of claim 16, further comprisingproviding a user interface whereby a user may search for an entity andretrieve relevant information based at least in part on the aggregatedpublic records of transactions.